Development Over Expected

Advanced Machine Learning Analysis of Athlete and Team Performance in College Football

Introduction

Goals:

A primary concern of athletic programs across the country is to provide a contextual framework for their athletes that will result in the athletes’ optimal physical development. Coaches rely upon previous experience, research, and proprietary data to create such plans. Without the ability to apply randomized experiments with their athletes, which would be unethical (a certain group receiving a less effective training regimen), coaches apply the regimen they believe to be most optimal without the ability to know if it truly was. A program may be put in place that has historically provided benefit to athletes, but may have been less efficacious compared to another program given the context of their team.

The goal of the development over expected framework is to use data available for all players and programs across the country, identifying teams, programs, and coaches that consistently develop their athletes at a high rate. The insights gained would help determine where a given program falls short in developing certain aspects of an athlete’s game and what training programs may be best suited to achieve the goals for the athlete and team.

Over-Expected Framework

The intuition behind the over expected frame work is to contextualize all aspects of an event, informing what the expected (or average) outcome would be given a plethora of factors. This methodology is often seen in football statistics such as “rush yards over expected” or “completion percentage over expected”. In the “rush yards over expected” statistic, factors such as down, distance, number of blockers, number of defenders in the box, etc. may be considered to inform the expected rush yards of a play. The contextualization of the event is only as granular as the data provided (i.e. play-by-play or frame-by-frame). Once the methodology for determining the expected value has been established we can find “rush yards over expected”(RYOE) with the follow equation:

\(\text{RYOE per attempt} = \frac{\sum_{i=1}^n (\text{observed rush yards})_i - (\text{expected rush yards})_i}{\text{attempts}}\)

The importance of a statistic of this nature is that it accounts for all factors that may impact an outcome, allowing us to understand how much better than average a player or team is within a certain domain. Applying this to athlete development is valuable given the many known and unknown factors that effect athlete performance. Beyond the individual, grouping athletes performances by team can inform us on how a given team generally develops their athletes.

Data Sources, Metrics, and Modeling

Data

Data was collected from a combination of sources including HudlIQ, Telemetry, PFF, and CollegeFootballData.com. Data from the following sources was aggregated to form a comprehensive data set used to contextually model FBS athlete performance from 2020-2025. Data was filtered to include athletes that had 25 or more tracked plays through a given season.

• HudlIQ:
  • Athlete 247 recruiting profile
• Telemetry:
  • Athlete biography
  • Athlete athleticism measures
    • Measured performance including top speed, acceleration, etc.
    • Modeled performance including estimated 10, 20, and 40, etc.
  • Team roster information
• PFF:
  • Player and team grades
• CollegeFootballData.com
  • Team performance measures including SRS, FPI, ELO, etc.

Metrics

Predictor Variables
Variable	Description
Weight	Current weight in pounds
BMI	Body Mass Index (\(kg/m^2\))
Change in BMI	BMI change from previous season
Position	QB, RB, WR, TE, OL, DT, DE, LB, S, CB
Year in College	Based on year recruited or remaining eligibility (1-6). Given available data, some athletes may be labeled incorrectly.
Years of Experience	Number of FBS seasons played in
Transfer Season	If the athlete transferred that season
Missed Time	If an athlete missed time due to redshirt, injury, or lack of playing time (60%+ reduction of prev. season snaps)
Group Change	If an athlete changed groups: Skill, Combo, Big
Recruiting Ranking	247 Composite Rating
Offer Score	A proprietary metric which evaluates the quality and quantity of a players offers.
Previous Performance (Lag 1)*	Performance value from previous season (NA if none)
Previous Performance (Lag 2)*	Performance value from second previous season (NA if none)
Note: * Not included in base model but included in lagged model

Outcome/Response Variables
Variable	Description
Max Acceleration	Top acceleration an athlete hit in a season (yds/s²)
Max Speed First 10 Yards	Top speed an athlete hit in the first 10 yards of a sprint in a season (mph)
Max Speed	Top speed an athlete hit in a season (mph)
Top 3 Average Speed	Average of an athletes top 3 fastest game reps (mph)
Top 5 Average Speed	Average of an athletes top 5 fastest game reps (mph)
Game Speed 10*	An athletes estimated 10 yard burst time
Game Speed 40*	An athletes estimated 40 yard dash time
Game Speed Flying 20*	An athletes estimated flying 20 yard time
Change of Direction	“How efficiently a player can change direction, focusing on high effort plays.” - Telemetry
Deceleration	“Specifically for receievers and defensive backs, this metric measures how many seconds faster or slower than average it took the player to decelerate” - Telemetry
DL Burst	“Assess a pass rushers ability to explode off the line of scrimmage” - Telemetry
Note: * These metrics are estimated using an athletes frame-by-frame speed and acceleration profile.

Metric Stability

The below table is an attempt to quantify the stability of outcome/response metrics. Ideally, we will see high Intra-Class Correlation (ICC) and lagged correlation.

ICC looks to quantify how much variability in our response variable can be attributed to differences between athletes and variability in athletes performances. A high ICC may indicate that a metric is more stable given the variability of the metric can be attributed to between athletes rather than within an athlete.

Lagged correlation looks to inform whether the value of a given year correlates with the previous year. A high lagged correlation will indicate year-to-year consistency within an athlete.

What is interesting to see is that the measures of overall ICC and lag correlation are larger than the average observed within position groups. This may indicate differences between position groups and just how volatile the metrics can be. When looking at overall or average, the order of metrics doesn’t change much.

Though these metrics provide insight to whether a given metric could be predicted more confidently, it is important to remember some metrics are inherently noisier than others. For example, we see great performance with the DL Burst metric for ICC and lagged correlation. However, when modeling, it faced difficulty finding meaningful relationships with the predictor variables, likely due to noise in the metric.

Modeling

To account for the longitudinal structure of an athlete’s career and the complex relationships between predictors and the response variable, a Gaussian Process Boosting (GPBoost) model was employed across all metrics.

GPBoost utilizes the LightGBM boosting framework from Microsoft to account for the complex structures within the data. To account for the longitudinal structure and athlete random effects, GPBoost uses Gaussian Processes to account for the different variance structures and estimate uncertainty.

Two models were trained across all metrics to account for two different kinds of performance/development:

1. Lagged Model

• Includes all predictors including previous/lagged performances from the previous two seasons. Including previous performances allows for better estimation of current season performances.

2. Base Model:

• Includes all predictors except lagged performance values. The goal of this model was to be independent of athlete, informing year-to-year development changes.

In the below plots we can see the predicted vs actual values for position group averages across college years for the lagged model. Ideally, points will lie on the dashed line, representing a 1-to-1 relationship that the models predictions represent the observed data. Generally, we see good alignment across plots, with the range of the axis’s showing minimal differences in scales.

Application to Athlete Development in Collegiate Football

Measuring Athlete Development

By using the two different models described above, we can measure two different aspects of athlete performance and development.

1. Over Expected Performance

Measured using the residual between actual performance and the prediction from the lagged model (horizontal comparison). This informs how an athlete is performing compared to expectation, given previous performances.

2. Over Expected Development

Measured by comparing actual year-to-year changes and predicted year-to-year changes from the base model (vertical differences). This informs how an athlete is improving year-over-year to what was expected.

The below table is a demonstration as to how the two different models are implemented.

Example Player Prediction and Assessment

Ex. 1: Emmett Johnson (Top 5 Average Speed)

The GPBoost model isn’t entirely interpretable, however, SHAP (Shapley Additive Explanations) values allows the impact of each predictor to be understood when predicting any given observation. The comparison of SHAP values across observations can give insight to how different values of the predictors move the needle when making a final prediction. This can be seen in the “Model Assessment” section for all metrics at the end of the report.

It is interesting to see the factors that motivated Emmett’s final top 5 average speed prediction. The biggest movers are his size followed by fine tuning through his previous speed and other contextual factors. A large positive random effect is observed, signaling that Emmett is faster than athletes with similar characteristics.

The below plot shows an athletes career performances compared to what was expected. The blue line is the actual performances and the dotted grey is the expectation. Green residuals are positive over performance and red is negative under performance. The size of points is representative of the number of snaps played.

We see early in Emmett’s career, he was performing just below what was expected. He took a significant jump his final season, outperforming what was expected by a significant margin.

This plot shows an athletes career performances compared to the expected trajectory. Green arrows show the athlete improved more than what the trajectory expected while red arrows show the athlete regressed more than what the trajectory expected. An athlete can show improvement but still have negative development. The size of points is representative of the number of snaps played.

Emmett’s development was right on track after his first measured season. Again, observed is a large jump in performance, showing significant development over expected.

Ex. 2: Marques Buford (Game Speed 40 / Estimated 40 Time)

When predicting Marques’s estimated 40 time, we see the primary mover is his size. Following we see many factors cancel each other out as they approach his final prediction. We notice a random effect of 0, signalling that Marques’s performances are on par with athletes of similar characteristics.

We see early in Marques’s career well-above-expected performances. In the following year, he experienced an injury, which can also be seen in the reduction in snaps played. Following the injury, we see a gradual return to expected performance levels, finally exceeding expectations in his final season. It is interesting to see that his final season projection falls off, indicating that older, underperforming athletes may not always rebound to previous levels. At each point, the model considers the previous two performances, so when analyzing a specific performance, it is important to look at what came prior.

Now looking at Marques’s development trends, we see a similar story. His injury season severely impacted his development, but in following years we actually see positive development. So despite under performing his expectations, he over performed his expected development in later years.

Over Expected Metrics and Athlete Football Performance

Athlete Football Performance Metrics
Metric	Definition
Shield Score (SS)	Proprietary metric that predominantly represents a players usage, production, and efficiency.
PFF Grade	A play-by-play performance grade for a players ability to complete their assignment.
twar	A measure of a players ability through their contribution of ‘wins’ compared to a replacement level player.
* Δ of the above metrics represents year-over-year changes in the value.

Comparing player success with measures of athleticism show some interesting trends. We primarily notice moderate correlations among offensive skill positions, particularly running back. Oppositely there doesn’t seem to be any trends with the linebacker position, potentially due to athleticism impacting success less or the difficulty in measuring success for that position.

	Player Performance Correlations: Raw Performance
	Top 5 Avg Speed						Game Speed 40
Position	PFF	SS	twar	ΔPFF	ΔSS	Δtwar	PFF	SS	twar	ΔPFF	ΔSS	Δtwar
CB	0.175	0.223	0.192	0.067	0.216	0.127	-0.036	-0.194	-0.057	0.005	-0.058	-0.049
DE	0.214	0.134	0.184	0.078	0.059	0.148	-0.075	-0.007	0.010	-0.112	0.061	-0.076
LB	-0.013	0.009	-0.042	0.013	-0.004	0.030	0.033	-0.084	0.028	-0.037	0.033	-0.016
RB	0.319	0.382	0.330	0.201	0.360	0.278	-0.257	-0.234	-0.224	-0.120	-0.037	-0.087
S	0.099	0.086	0.118	0.072	0.165	0.096	-0.052	-0.108	-0.022	0.018	-0.035	0.101
TE	0.158	0.330	0.308	0.013	0.294	0.140	-0.300	-0.294	-0.311	-0.095	-0.111	-0.144
WR	0.251	0.363	0.312	0.123	0.243	0.182	-0.203	-0.254	-0.195	-0.112	-0.083	-0.078
MEAN	0.176	0.218	0.212	0.081	0.192	0.143	0.137	0.168	0.121	0.071	0.060	0.079

Now comparing over expected performance, we see a similar result in terms of positions with the stronger correlations. Isolating a singular value, we see a moderately strong relationship between running back over expected performance and positive change in shield score. Showing that running backs that outperform their speed expectation may also produce more on the field. A similar trend for tight ends, receivers, and cornerbacks though the relationship isn’t as strong.

	Player Performance Correlations: OE Performance
	Top 5 Avg Speed						Game Speed 40
Position	PFF	SS	twar	ΔPFF	ΔSS	Δtwar	PFF	SS	twar	ΔPFF	ΔSS	Δtwar
CB	0.108	0.089	0.145	0.064	0.228	0.146	-0.032	-0.112	-0.066	-0.003	-0.083	-0.068
DE	0.123	0.051	0.150	0.083	0.021	0.159	-0.048	0.023	-0.010	-0.113	0.117	-0.049
LB	0.012	-0.051	-0.010	0.055	0.037	0.057	-0.043	-0.067	-0.031	-0.084	-0.020	-0.079
RB	0.264	0.269	0.263	0.259	0.425	0.321	-0.241	-0.175	-0.210	-0.209	-0.120	-0.156
S	0.043	-0.043	0.055	0.062	0.122	0.061	-0.055	-0.061	-0.030	0.013	-0.015	0.082
TE	0.077	0.177	0.213	-0.007	0.272	0.164	-0.225	-0.211	-0.238	-0.159	-0.089	-0.179
WR	0.185	0.235	0.229	0.143	0.282	0.222	-0.178	-0.185	-0.168	-0.156	-0.093	-0.108
MEAN	0.116	0.131	0.152	0.096	0.198	0.161	0.117	0.119	0.108	0.105	0.077	0.103

For over expected development, the most noticeable trend is now the correlations shifting from in-season performances to year-over-year changes in performance. This shows that athletes that develop their athleticism more than expected also tend to improve their football performances. Again, most noticeable in running backs. The trend is also noticeable in the PFF grade, representing ability to handle on-field assignments better.

	Player Performance Correlations: OE Development
	Top 5 Avg Speed						Game Speed 40
Position	PFF	SS	twar	ΔPFF	ΔSS	Δtwar	PFF	SS	twar	ΔPFF	ΔSS	Δtwar
CB	0.089	0.034	0.112	0.097	0.185	0.157	-0.070	-0.051	-0.074	-0.021	-0.100	-0.088
DE	0.020	0.002	0.046	0.110	0.001	0.164	0.018	0.088	0.032	-0.119	0.058	-0.035
LB	-0.015	-0.096	-0.024	0.062	0.062	0.034	0.033	0.035	0.013	-0.051	-0.037	-0.044
RB	0.149	0.143	0.152	0.274	0.380	0.288	-0.149	-0.060	-0.130	-0.316	-0.252	-0.215
S	-0.004	-0.054	0.031	0.038	0.056	0.047	-0.038	-0.021	-0.015	0.012	-0.023	0.057
TE	0.037	0.057	0.109	-0.003	0.212	0.155	-0.090	-0.065	-0.101	-0.171	-0.099	-0.144
WR	0.096	0.133	0.142	0.154	0.301	0.243	-0.123	-0.098	-0.112	-0.203	-0.118	-0.132
MEAN	0.059	0.074	0.088	0.105	0.171	0.155	0.074	0.060	0.068	0.128	0.098	0.102

Assessing Team Ability

Team Level Analysis

To account for a teams ability a few different methods were considered to analyze a teams raw athleticism, over expected performance, and over expected development.

Team Athleticism/Development Metrics
Method	Definition	Purpose
Trimmed Mean	The teams average value after removing a small percentage of outliers from both ends.	Removes the influence of extreme outliers but still accounts for a team’s general performance.
Estimated Team Effect	Similar to a teams mean, except is adjusted for other factors through a regression based method.	Some results may be biased from factors such as age and position, this method removes this influence to only account for team effect.
Prob +*	The probability a teams observed values are greater than the observed population median.	Gives insight on the general positive effect of a team across their roster. At what rate are their athletes above the population median?
Prob SWC*	The probability a teams observed values are greater than the observed population smallest worthwhile change.	Research around smallest worthwhile change (or decrement) was used to inform whether an athlete’s improvement was more than just noise and represented a genuine change in percentile rank. Being greater than the population positive smallest worthwhile change can represent greater/significant positive impact
Prob SWD*	The probability a teams observed values are greater than the observed population smallest worthwhile decrement.	Being less than the population negative smallest worthwhile change can represent greater/significant negative impact.
Note:
* Team distributions are estimated using Kernel Density Estimation.

The below plots are a representation of how the team level metrics are produced, showing Nebraska’s performance across raw performance, over expected performance, and over expected development for top 5 average speed. Reiterating from the above table, the dark green region is probability of significant positive impact, light green is probability of positive impact, and red is probability of significant negative impact. The dashed vertical lines are the estimated team mean and team effect.

Over Expected Metrics and Team Football Performance

Team Football Performance Metrics
Metric	Definition	Purpose
Win %	Proportion of wins.	A general representation of team ability that is easily interpretable.
Simple Rating System (SRS)	An adjusted point differential rating system accounting for opponent strength.	A step up from win % that now accounts for the game outcome context and opponent.
Football Power Index (FPI)	From ESPN, ‘… represents how many points above or below average a team is.’	Somewhat of a black-box metric, but may be considered as a more advanced SRS.
ELO	A measure of program historical strength, updated and maintained on a game-by-game basis.	Gives insight to the current and historical success of a program.
Against the Spread Win %	Proportion of games the team covered the spread betting line.	Spreads are a unique way to assess teams on an even playing field. A team may not win, but covering the spread can indicate an over expected performance.
Average Cover Margin	The average number of points a team covered (or didn’t cover) the spread betting line by.	Similar to SRS but applied contextually with spreads. Represents average point differential on an even playing field.
Average Absolute Correlation	The average absolute value of correlations across the above ratings/metrics.	Of the calculated over expected metrics, which are producing the most signal among team perfromance measures.

Following the correlations between players and performance, it is interesting to see much stronger trends between teams and their overall athletic performance. Below, the trends you would expect to see are present, metrics associated with higher levels of athleticism correlate moderately to strongly with team success measures. This would confirm the idea that more athletic teams tend to perform better on the field.

Team Performance Correlations: Raw Performance (Top 3 Avg. Speed)
Metric	Win %	SRS	FPI	ELO	ATS Win %	Avg. Cover	Avg. Abs. Corr.
Prob SWC	0.413	0.406	0.430	0.445	0.262	0.300	0.376
Prob +	0.403	0.353	0.369	0.388	0.287	0.343	0.357
Prob SWD	-0.363	-0.294	-0.302	-0.318	-0.311	-0.343	0.322
Team Effect	0.347	0.255	0.267	0.302	0.337	0.376	0.314

Now looking at over expected performance, it is interesting to see stronger trends with betting performance measures than the teams actual success measures. Given these metrics are associated with over expected football performance, it’s no surprise they correlate with over expected athletic performance.

Team Performance Correlations: OE Performance (Top 3 Avg. Speed)
Metric	Win %	SRS	FPI	ELO	ATS Win %	Avg. Cover	Avg. Abs. Corr.
Prob +	0.105	-0.174	-0.163	-0.066	0.298	0.296	0.184
Team Effect	0.132	-0.148	-0.144	-0.038	0.310	0.331	0.183
Trimmed Mean	0.120	-0.166	-0.160	-0.051	0.289	0.312	0.183
Prob SWC	0.056	-0.226	-0.209	-0.095	0.223	0.223	0.172
Prob SWD	-0.119	0.091	0.089	0.022	-0.285	-0.300	0.151

Looking at over-expected development, we see some different but similar trends to over-expected performance. We see moderate to strong negative correlations with SRS and FPI, indicating that positive over-expected development is associated with lower (or negative) point differentials. However, there is no correlation with win percentage. This could indicate that teams that develop their athletes more than expected are able to keep games closer, but not necessarily win more games. This could be indicative of teams with less overall talent being able to compete better through development, but ultimately talent wins out in the end. This may also be seen in the minimal positive correlations with betting-based performance measures. Where teams are performing better than their expected spread lines.

Team Performance Correlations: OE Development (Top 3 Avg. Speed)
Metric	Win %	SRS	FPI	ELO	ATS Win %	Avg. Cover	Avg. Abs. Corr.
Prob SWC	-0.069	-0.377	-0.374	-0.251	0.067	0.132	0.212
Team Effect	0.062	-0.251	-0.251	-0.121	0.121	0.200	0.168
Trimmed Mean	0.064	-0.235	-0.237	-0.115	0.123	0.202	0.163
Prob +	0.009	-0.234	-0.240	-0.141	0.090	0.183	0.149
Prob SWD	-0.154	0.001	0.010	-0.060	-0.088	-0.178	0.082

Additional Findings and Exploration:

The below findings can help interpret team performances and give motivation to future projects. We may be able to draw further conclusions from player and team rankings by understanding the context and potential biases behind predictions.

Group of 4 v. Power 6 Development Trends

This plot shows the distributions of top 5 average speed across college years, comparing Group of 6 and Power 4 athletes. What is noticeable is that the distributions differ most significantly for year 1 athletes, with Power 4 athletes being faster on average. The general spread of the distributions is similar, showing that both groups have similar variability and ability to produce elite athletes. Up until year 4, we notice even slight shifts to the right for Power 4 athletes, indicating that they are still faster on average. However, as college year increases, the distributions become more similar, indicating that Group of 6 athletes are closing the athleticism gap as they mature.

This plot represents projected vs. actual development across college years, comparing Power 4 and Group of 6 athletes in terms of top 5 average speed. The dashed line indicates where actual development equals projected development. Points above the line indicate athletes who developed more than expected, while points below indicate less than expected development.

What is interesting to notice, particularly in early college years, is the clumping of Group of 6 (blue) athletes in the top right of the plot. This is showing that Group of 6 athletes are are projected to develop more and are actually developing more compared to Power 4 athletes. This trend continues but less noticeable as college year increases. This could be indicative of a “late-bloomer” effect in Group of 6 athletes, where they ultimately just needed more time to mature athletically. Young Group of 6 athletes have a proportionally higher athletic ceiling compared to Power 4 athletes. This could help inform transfer recruiting by flagging athletes that that have experienced significant growth (or who are about to).

The general conclusion from both the above plots is that athletes entering Power 4 programs tend to be close to finished products, having approached their physical potential earlier. Given this, we could expect growth from athletes as they enter a collegiate training program, informing that less weight could be placed on athleticism coming out of high school.

Team Experience

At the athlete level, there do not seem to be many strong relationships between age or experience and athletic development or football performance. There seems to be a minimal contrasting relationship between age and YOE ratio (experience/college years) with raw performance. This suggests proportionally more experienced athletes may be more athletic. The strongest relationship appears to be between the YOE ratio and Shield Score. Shield Score projects an athlete’s draft ability, so it makes sense that a proportionally more experienced athlete would have a higher score.

	Athlete Age/Experience Correlation w/ Performance
	Athletic (Top 3 Avg Speed)			Football
	Raw Value	OE Performance	OE Development	Shield Score	PFF Grade	TWAR
Age	-0.141	-0.022	0.014	-0.033	0.012	0.003
Experience	-0.017	-0.011	0.043	0.166	0.103	0.049
YOE Ratio	0.103	-0.001	0.044	0.256	0.132	0.062

It isn’t entirely shocking to see that teams of greater experience (particularly proportional to their age) also possess greater returning production. It is interesting to see, however, that age doesn’t signal this, but actually displays an opposite trend. The older a team gets, returning production may be lower due to many inexperienced athletes having sat on the roster.

Age/Experience Correlation w/ Returning Production
	Returning Production
Age	-0.203
Experience	0.459
YOE Ratio	0.508

Comparing age and experience with team based athleticism metrics shows other interesting trends. The dominating trend ends up being experience proportional to age. This indicates that experience relative to age is more important than experience alone (For example, a 2nd year athlete that played their first year or a 4th year athlete that played 2 prior seasons would be equivalent with a YOE ratio of 0.5).

We see a general trend that proportionally more experienced teams possess greater athleticism. Displaying trends that higher levels of athleticism are more common among experienced rosters. With a weak, but opposite trend for older rosters.

In terms of over performing expectation, more experienced teams, though a weak trend, seem to potentially under perform their expectations. This is likely due to them already being closer to their athletic potential.

Finally, in terms of development, we notice even stronger negative trends among experienced teams. This shows that inexperienced teams are more likely to develop and exceed their expected development. The slight negative trend in regards to significant negative development for experienced teams would indicate that they tend to exist in the area between median development and significant negative development. Indicating they are inline to just below expectations.

	Age/Experience Correlation w/ Team Athletic Metrics
	Target Metrics				OE Performance				OE Development
	Avg	Prob SWD	Prob +	Prob SWC	Avg	Prob SWD	Prob +	Prob SWC	Avg	Prob SWD	Prob +	Prob SWC
Age	-0.088	0.098	-0.053	-0.084	0.207	-0.117	0.203	0.251	0.327	-0.122	0.313	0.386
Experience	0.260	-0.190	0.243	0.285	-0.148	0.041	-0.083	-0.212	-0.110	-0.157	-0.106	-0.295
YOE Ratio	0.288	-0.223	0.258	0.313	-0.210	0.078	-0.151	-0.288	-0.228	-0.102	-0.220	-0.428
RetProd	0.148	-0.131	0.189	0.142	-0.098	-0.002	-0.068	-0.193	-0.192	-0.009	-0.177	-0.346

Comparing age and experience with team success metrics also yields interesting results. Again, the dominating trend ends up being experience proportional to age, indicating that experience relative to age is more important than experience alone. We see predominate correlations with SRS and FPI which consider contextual and opponent strength factors to a teams ability that win percentage alone does not. This would signal that proportionally experienced rosters perform above the expectation on the football field. Given this, it is interesting to see that no trends are observed with betting based metrics.

When examining the relationship between age and experience with team football performance, we again see the YOE ratio produce the strongest signal. This reinforces that what matters most is not simply how much experience athletes have accumulated, but rather how the experience aligns with their age. The clearest associations appear with SRS and FPI, metrics that incorporate opponent strength and contextual factors beyond raw win percentage. This suggests that teams with higher proportions of experienced-relative-to-age athletes tend to outperform baseline expectations on the field. Curiously, this advantage does not translate to betting-based performance measures, where no meaningful relationships are observed.

	Age/Experience Correlation w/ Team Perfromance
	Win %	SRS	FPI	ELO	ATS Win %	Avg Cover Margin
Age	-0.218	-0.429	-0.462	-0.397	0.147	0.077
Experience	0.360	0.647	0.641	0.538	0.081	0.127
YOE Ratio	0.422	0.771	0.781	0.661	0.021	0.084
RetProd	0.276	0.434	0.418	0.327	0.096	0.103

Graphically, it is more powerful to see the correlation flip between age and YOE ratio when compared to team success metrics. Not only does the trend flip, but it is actually much stronger, possessing greater signal.

The Transfer Effect

As the transfer portal grows and teams race to maintain and acquire talent, understanding the general trends surrounding transferring athletes and their athletic development is important. In the table below, we see most transfers occur laterally among Power 4 programs. Similar numbers are present for transfers up, down, and laterally in the Group of 6.

Numbers based on athletes w/ playing experience for 2023-2025

Transfer Type Counts
Transfer Type	Count	Proportion(%)
Lateral (P4)	402	33
Down	295	25
Up	255	21
Lateral (G6)	251	21

The below plot compares transfer types to one another on the basis of their top 5 average speed the season following a transfer. Across college years, a clear trend isn’t quite distinguishable. An interesting trend that does support previous claims of a Group of 6 late-bloomer effect is the “Up”(green) being generally higher for earlier years compared to other groups.

A statistical F-test determined transfer types to differ insignificantly, however, an interaction between college year and transfer type did exist. This is seen in the almost random fluctuation year-to-year. An interaction means that the influence of one variable on another, depends on the level of that variable. For example, the effect of transferring “Up” may appear significantly different across different college years.

This plot now looks to answer if different transfer types have a better or worse over expected performance. It would appear that across college years, transfer type does not seem to differ. The statistical F-test does, however, return a barely significant result (F = 2.3, P = 0.05). When looking at the pairwise comparisons, the one significant comparison is “Lateral (P4)” being significantly worse than “Non-Transfer”. This could be indicative of athletes transferring laterally in Power 4 programs not performing as well as non-transferring athletes. This could be used as argument to maintain players within your system rather than bringing in lateral transfers. However, given the lack of clear trends in the plot, this conclusion should be taken with caution.

The final plot now looks to compare transfer type with over expected development. In this analysis we see the clearest and most consistent results. Across college years, we see a decreasing or reduced development effect starting at the “Down” group to the “Up” group. I believe this to be representative of less experienced athletes transferring and potentially moving down to Group of 6. It was shown previously that less experienced athletes tend to develop more compared to more experienced athletes.

A statistical F-test determined a significant difference between transfer types without an interaction with college year (F = 13.671, p << 0). There were many significant pairwise differences but the most interesting was that the “Up” group was significantly smaller compared to all groups, showing worse over expected development. Also, the “Non-Transfer” group didn’t significantly differ from the “Lateral (P4)” or “Lateral (G6)” groups.

Athlete Weight Optimization

A unique feature of this project is that the inputs can be manipulated to project future performances. As a result, we can predict through different weights, BMIs, and changes in BMI that could optimize performance through different ranges of weight gain and loss. This can inform athletes on how much weight can be gained (or lossed) and would still allow for peak performance.

For the below examples, we would like to find the largest weight an athlete can gain for next season while still improving (or at least maintaining) their speed performance. Using the lagged model, we can input different weight values and see how the predicted speed changes. Additionally, we can see how these changes would impact their percentile rank among their position group.

Ex. 1: Jacory Barney Jr. (Top 3 Avg. Speed)

We see Jacory could put on upwards of 11 pounds going into next year. This would move his top 3 average speed percentile from the 56th to the 62nd percentile, improving his speed from 19.9 to 20.15 mph.

Ex. 2: Amare Sanders (Top 3 Average Speed)

Similarly, we see Amare could gain upwards of 8 pounds going into next year. This would move his top 3 average speed percentile from the 37th to the 42nd percentile, improving his speed from 19.68 to 19.82 mph.

Next Steps and Future Directions

• Display position specific stability measures across metrics.

• Create a team ranking system that aggregates across multiple metrics to evaluate teams according to overall athleticism impact.

• Create interactive shiny app to allow users to explore athlete and team level data and predictions.

• Further explore application of weight optimization

• Develop a method for player flagging/evaluation.

• Change implementation of transfer predictor to account for type of transfer rather than just an indication of true or false.

• Potentially try and rework the college year label. Difficult to account for given the extended eligibility of athletes.

• Explore a centered lagged variable or cumulative mean rather than just using raw lagged values. This is after observing bizarre model performance in both DL Burst models that need to be reassessed. I have included the raw value rankings but not over expected performance or development due to this.

Team Rankings

Big Ten Raw Performance Summary: Max Accel | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	15.553 (4, 1)	14.717 (7, 1)	0.765 (5, 1)	0.517 (5, 1)	0.058 (1, 1)
Iowa	3.78 (59) / 0.43 (67)	14.190 (68, 2)	13.662 (80, 5)	0.482 (73, 3)	0.317 (52, 3)	0.344 (92, 4)
Ohio State	3.1 (4) / 0.48 (92)	14.050 (83, 3)	13.555 (98, 8)	0.464 (81, 4)	0.323 (50, 2)	0.384 (109, 7)
Illinois	3.58 (31) / 0.46 (84)	14.000 (84, 4)	13.783 (70, 3)	0.495 (66, 2)	0.245 (87, 7)	0.276 (60, 2)
Penn State	3.49 (23) / 0.48 (92)	13.976 (88, 5)	13.605 (86, 6)	0.463 (82, 5)	0.233 (94, 8)	0.311 (75, 3)
UCLA	3.93 (81) / 0.43 (67)	13.909 (92, 6)	13.818 (69, 2)	0.385 (107, 11)	0.230 (97, 10)	0.384 (110, 8)
Wisconsin	3.77 (57) / 0.41 (61)	13.871 (93, 7)	13.469 (104, 11)	0.408 (101, 8)	0.251 (84, 6)	0.399 (113, 10)
Oregon	3.54 (29) / 0.5 (97)	13.870 (95, 8)	13.595 (89, 7)	0.421 (95, 6)	0.270 (72, 5)	0.393 (112, 9)
USC	3.3 (11) / 0.46 (84)	13.843 (99, 9)	13.664 (79, 4)	0.399 (102, 9)	0.285 (66, 4)	0.442 (125, 14)
Minnesota	3.7 (47) / 0.44 (72)	13.804 (103, 10)	13.506 (100, 9)	0.408 (100, 7)	0.211 (105, 12)	0.360 (103, 6)
Nebraska	3.44 (19) / 0.39 (50)	13.667 (112, 11)	13.481 (102, 10)	0.371 (112, 12)	0.205 (109, 13)	0.401 (115, 11)
Rutgers	3.78 (59) / 0.47 (89)	13.667 (113, 12)	13.244 (126, 14)	0.363 (116, 14)	0.127 (125, 14)	0.344 (93, 5)
Indiana	3.92 (80) / 0.47 (89)	13.587 (117, 13)	13.223 (128, 15)	0.393 (106, 10)	0.231 (96, 9)	0.421 (119, 12)
Michigan	3.36 (15) / 0.42 (64)	13.496 (123, 14)	13.384 (115, 13)	0.239 (133, 17)	0.114 (129, 16)	0.455 (129, 16)
Michigan State	3.76 (55) / 0.45 (79)	13.477 (125, 15)	13.200 (130, 16)	0.368 (114, 13)	0.219 (103, 11)	0.458 (130, 17)
Washington	3.53 (28) / 0.39 (50)	13.440 (128, 16)	13.443 (108, 12)	0.252 (132, 16)	0.100 (131, 17)	0.429 (123, 13)
Purdue	3.52 (26) / 0.4 (53)	13.424 (129, 17)	13.076 (133, 18)	0.280 (127, 15)	0.123 (126, 15)	0.445 (126, 15)
Maryland	2.98 (2) / 0.37 (43)	13.185 (133, 18)	13.079 (132, 17)	0.211 (134, 18)	0.062 (133, 18)	0.539 (134, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Max Accel | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.966 (8, 1)	0.652 (8, 1)	0.739 (6, 1)	0.506 (6, 1)	0.111 (5, 1)
UCLA	3.93 (81) / 0.43 (67)	0.244 (55, 2)	0.294 (56, 2)	0.495 (70, 3)	0.310 (59, 4)	0.316 (80, 5)
USC	3.3 (11) / 0.46 (84)	0.214 (58, 3)	0.262 (63, 4)	0.468 (79, 4)	0.349 (41, 2)	0.381 (108, 9)
Illinois	3.58 (31) / 0.46 (84)	0.210 (60, 4)	0.262 (62, 3)	0.591 (25, 2)	0.339 (47, 3)	0.266 (47, 2)
Oregon	3.54 (29) / 0.5 (97)	0.037 (74, 5)	0.186 (76, 5)	0.467 (80, 5)	0.267 (77, 6)	0.313 (79, 4)
Penn State	3.49 (23) / 0.48 (92)	-0.064 (87, 6)	0.128 (87, 6)	0.465 (81, 6)	0.252 (86, 8)	0.329 (90, 6)
Michigan	3.36 (15) / 0.42 (64)	-0.108 (93, 7)	0.109 (94, 8)	0.361 (117, 11)	0.169 (120, 14)	0.301 (75, 3)
Ohio State	3.1 (4) / 0.48 (92)	-0.116 (94, 8)	0.110 (93, 7)	0.360 (119, 12)	0.285 (69, 5)	0.465 (129, 16)
Iowa	3.78 (59) / 0.43 (67)	-0.141 (96, 9)	0.096 (98, 9)	0.370 (114, 10)	0.165 (122, 15)	0.362 (101, 7)
Wisconsin	3.77 (57) / 0.41 (61)	-0.161 (99, 10)	0.095 (99, 10)	0.412 (101, 8)	0.261 (78, 7)	0.432 (123, 12)
Washington	3.53 (28) / 0.39 (50)	-0.173 (101, 11)	0.070 (103, 11)	0.414 (99, 7)	0.225 (95, 9)	0.379 (107, 8)
Nebraska	3.44 (19) / 0.39 (50)	-0.237 (111, 12)	0.035 (113, 12)	0.388 (110, 9)	0.210 (100, 10)	0.392 (112, 10)
Minnesota	3.7 (47) / 0.44 (72)	-0.279 (114, 13)	0.028 (114, 13)	0.325 (127, 16)	0.187 (113, 12)	0.459 (127, 14)
Purdue	3.52 (26) / 0.4 (53)	-0.434 (127, 14)	-0.051 (127, 14)	0.349 (122, 13)	0.139 (129, 16)	0.403 (116, 11)
Rutgers	3.78 (59) / 0.47 (89)	-0.440 (128, 15)	-0.051 (128, 15)	0.230 (134, 18)	0.065 (134, 18)	0.445 (126, 13)
Michigan State	3.76 (55) / 0.45 (79)	-0.455 (129, 16)	-0.064 (129, 16)	0.331 (125, 15)	0.201 (103, 11)	0.488 (133, 18)
Maryland	2.98 (2) / 0.37 (43)	-0.499 (130, 17)	-0.098 (132, 18)	0.314 (129, 17)	0.136 (130, 17)	0.463 (128, 15)
Indiana	3.92 (80) / 0.47 (89)	-0.538 (132, 18)	-0.095 (131, 17)	0.340 (123, 14)	0.184 (115, 13)	0.469 (130, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Max Accel | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	1.017 (3, 1)	0.494 (3, 1)	0.700 (4, 1)	0.475 (7, 1)	0.137 (3, 2)
USC	3.3 (11) / 0.46 (84)	0.497 (17, 2)	0.350 (16, 2)	0.601 (23, 2)	0.447 (11, 2)	0.277 (52, 6)
Oregon	3.54 (29) / 0.5 (97)	0.281 (31, 3)	0.290 (31, 3)	0.544 (40, 3)	0.341 (46, 5)	0.205 (18, 3)
UCLA	3.93 (81) / 0.43 (67)	0.261 (33, 4)	0.283 (36, 4)	0.512 (60, 6)	0.346 (41, 3)	0.309 (76, 10)
Maryland	2.98 (2) / 0.37 (43)	0.089 (45, 5)	0.249 (45, 5)	0.498 (70, 10)	0.284 (77, 7)	0.278 (54, 7)
Michigan	3.36 (15) / 0.42 (64)	0.046 (51, 6)	0.232 (52, 6)	0.438 (104, 13)	0.222 (112, 12)	0.115 (2, 1)
Ohio State	3.1 (4) / 0.48 (92)	-0.048 (60, 7)	0.207 (61, 7)	0.505 (66, 9)	0.322 (57, 6)	0.298 (69, 9)
Nebraska	3.44 (19) / 0.39 (50)	-0.078 (64, 8)	0.205 (64, 8)	0.543 (41, 4)	0.274 (83, 10)	0.248 (35, 4)
Rutgers	3.78 (59) / 0.47 (89)	-0.095 (70, 9)	0.198 (71, 9)	0.508 (65, 8)	0.264 (89, 11)	0.249 (36, 5)
Penn State	3.49 (23) / 0.48 (92)	-0.167 (78, 10)	0.180 (79, 10)	0.539 (43, 5)	0.279 (80, 9)	0.296 (66, 8)
Illinois	3.58 (31) / 0.46 (84)	-0.284 (91, 11)	0.144 (90, 11)	0.509 (64, 7)	0.343 (44, 4)	0.316 (82, 12)
Michigan State	3.76 (55) / 0.45 (79)	-0.515 (107, 12)	0.088 (106, 12)	0.440 (101, 11)	0.203 (118, 13)	0.313 (80, 11)
Indiana	3.92 (80) / 0.47 (89)	-0.689 (115, 13)	0.046 (116, 13)	0.440 (102, 12)	0.282 (78, 8)	0.392 (113, 15)
Purdue	3.52 (26) / 0.4 (53)	-0.833 (123, 14)	0.003 (122, 14)	0.376 (122, 14)	0.156 (132, 17)	0.373 (110, 14)
Iowa	3.78 (59) / 0.43 (67)	-0.956 (128, 15)	-0.027 (129, 15)	0.345 (128, 16)	0.166 (129, 15)	0.415 (121, 16)
Wisconsin	3.77 (57) / 0.41 (61)	-1.025 (131, 16)	-0.037 (131, 16)	0.346 (127, 15)	0.086 (134, 18)	0.348 (100, 13)
Minnesota	3.7 (47) / 0.44 (72)	-1.054 (132, 17)	-0.053 (132, 17)	0.323 (132, 17)	0.166 (130, 16)	0.460 (133, 17)
Washington	3.53 (28) / 0.39 (50)	-1.349 (134, 18)	-0.137 (134, 18)	0.304 (134, 18)	0.173 (126, 14)	0.467 (134, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Max Speed First 10yds | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	14.297 (18, 1)	13.917 (27, 1)	0.651 (7, 1)	0.414 (10, 1)	0.168 (8, 1)
Indiana	3.92 (80) / 0.47 (89)	14.109 (31, 2)	13.831 (43, 2)	0.470 (83, 6)	0.274 (73, 4)	0.262 (50, 4)
Iowa	3.78 (59) / 0.43 (67)	13.970 (53, 3)	13.684 (71, 7)	0.635 (10, 2)	0.224 (102, 9)	0.193 (17, 2)
Nebraska	3.44 (19) / 0.39 (50)	13.898 (60, 4)	13.815 (48, 3)	0.544 (41, 3)	0.324 (47, 3)	0.270 (57, 7)
Minnesota	3.7 (47) / 0.44 (72)	13.857 (68, 5)	13.733 (62, 4)	0.500 (65, 4)	0.361 (33, 2)	0.356 (103, 10)
Penn State	3.49 (23) / 0.48 (92)	13.791 (74, 6)	13.697 (68, 5)	0.498 (68, 5)	0.269 (79, 5)	0.269 (56, 6)
Maryland	2.98 (2) / 0.37 (43)	13.710 (83, 7)	13.637 (80, 8)	0.394 (116, 11)	0.238 (96, 7)	0.373 (115, 11)
Rutgers	3.78 (59) / 0.47 (89)	13.651 (92, 8)	13.465 (103, 11)	0.465 (87, 8)	0.253 (87, 6)	0.312 (78, 9)
Michigan	3.36 (15) / 0.42 (64)	13.635 (96, 9)	13.694 (69, 6)	0.352 (126, 15)	0.195 (121, 14)	0.269 (55, 5)
Oregon	3.54 (29) / 0.5 (97)	13.610 (98, 10)	13.564 (92, 10)	0.465 (86, 7)	0.197 (120, 13)	0.246 (40, 3)
Wisconsin	3.77 (57) / 0.41 (61)	13.510 (105, 11)	13.431 (109, 12)	0.335 (129, 17)	0.200 (118, 12)	0.304 (74, 8)
Washington	3.53 (28) / 0.39 (50)	13.431 (115, 12)	13.567 (90, 9)	0.390 (117, 12)	0.227 (100, 8)	0.398 (121, 15)
Ohio State	3.1 (4) / 0.48 (92)	13.353 (116, 13)	13.272 (123, 17)	0.395 (114, 9)	0.217 (106, 10)	0.381 (120, 14)
Michigan State	3.76 (55) / 0.45 (79)	13.329 (118, 14)	13.331 (120, 15)	0.361 (123, 14)	0.209 (111, 11)	0.414 (123, 16)
Illinois	3.58 (31) / 0.46 (84)	13.266 (123, 15)	13.345 (117, 14)	0.395 (115, 10)	0.172 (128, 16)	0.375 (116, 12)
Purdue	3.52 (26) / 0.4 (53)	13.249 (124, 16)	13.145 (132, 18)	0.366 (122, 13)	0.148 (132, 17)	0.380 (118, 13)
USC	3.3 (11) / 0.46 (84)	13.172 (126, 17)	13.324 (121, 16)	0.234 (134, 18)	0.091 (133, 18)	0.436 (129, 17)
UCLA	3.93 (81) / 0.43 (67)	13.165 (127, 18)	13.364 (115, 13)	0.335 (128, 16)	0.177 (127, 15)	0.438 (130, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Max Speed First 10yds | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.210 (39, 1)	0.095 (38, 1)	0.601 (16, 1)	0.313 (60, 6)	0.172 (10, 1)
Michigan	3.36 (15) / 0.42 (64)	0.202 (40, 2)	0.094 (40, 2)	0.505 (66, 6)	0.320 (56, 4)	0.273 (72, 6)
Maryland	2.98 (2) / 0.37 (43)	0.129 (56, 3)	0.089 (59, 4)	0.513 (61, 4)	0.345 (40, 1)	0.316 (95, 11)
Indiana	3.92 (80) / 0.47 (89)	0.084 (66, 4)	0.091 (50, 3)	0.511 (63, 5)	0.311 (61, 7)	0.279 (76, 7)
Oregon	3.54 (29) / 0.5 (97)	0.084 (67, 5)	0.086 (67, 5)	0.498 (71, 7)	0.250 (95, 11)	0.174 (11, 2)
Minnesota	3.7 (47) / 0.44 (72)	0.071 (69, 6)	0.085 (70, 7)	0.486 (78, 9)	0.345 (41, 2)	0.357 (119, 15)
Penn State	3.49 (23) / 0.48 (92)	0.070 (70, 7)	0.085 (69, 6)	0.546 (42, 2)	0.315 (59, 5)	0.255 (55, 5)
Washington	3.53 (28) / 0.39 (50)	0.062 (75, 8)	0.084 (73, 8)	0.482 (81, 10)	0.309 (64, 8)	0.328 (105, 13)
Nebraska	3.44 (19) / 0.39 (50)	0.043 (79, 9)	0.083 (78, 9)	0.513 (60, 3)	0.343 (43, 3)	0.323 (101, 12)
Iowa	3.78 (59) / 0.43 (67)	-0.022 (92, 10)	0.079 (88, 10)	0.442 (106, 12)	0.269 (83, 9)	0.298 (84, 8)
Wisconsin	3.77 (57) / 0.41 (61)	-0.025 (93, 11)	0.078 (93, 11)	0.490 (75, 8)	0.231 (104, 13)	0.245 (44, 4)
Rutgers	3.78 (59) / 0.47 (89)	-0.035 (95, 12)	0.077 (95, 12)	0.365 (125, 16)	0.180 (125, 14)	0.358 (120, 16)
USC	3.3 (11) / 0.46 (84)	-0.082 (101, 13)	0.073 (102, 13)	0.398 (121, 14)	0.153 (129, 16)	0.220 (32, 3)
UCLA	3.93 (81) / 0.43 (67)	-0.102 (107, 14)	0.072 (107, 14)	0.441 (107, 13)	0.262 (86, 10)	0.360 (121, 17)
Illinois	3.58 (31) / 0.46 (84)	-0.141 (113, 15)	0.069 (113, 15)	0.472 (90, 11)	0.237 (101, 12)	0.299 (85, 9)
Purdue	3.52 (26) / 0.4 (53)	-0.269 (126, 16)	0.055 (129, 18)	0.379 (124, 15)	0.130 (131, 18)	0.309 (91, 10)
Ohio State	3.1 (4) / 0.48 (92)	-0.285 (127, 17)	0.058 (128, 17)	0.360 (127, 17)	0.144 (130, 17)	0.335 (108, 14)
Michigan State	3.76 (55) / 0.45 (79)	-0.310 (129, 18)	0.061 (123, 16)	0.250 (133, 18)	0.156 (128, 15)	0.441 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Max Speed First 10yds | 2024 - 2025 | OL / DT / DE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Rutgers	3.78 (59) / 0.47 (89)	0.514 (10, 1)	0.357 (9, 1)	0.671 (5, 2)	0.273 (82, 9)	0.056 (1, 1)
Maryland	2.98 (2) / 0.37 (43)	0.454 (14, 2)	0.341 (13, 2)	0.584 (22, 3)	0.381 (17, 1)	0.198 (20, 3)
Oregon	3.54 (29) / 0.5 (97)	0.338 (18, 3)	0.305 (18, 3)	0.709 (2, 1)	0.325 (51, 4)	0.144 (6, 2)
Northwestern	3.86 (72) / 0.42 (64)	0.274 (25, 4)	0.289 (27, 4)	0.569 (30, 4)	0.315 (62, 5)	0.205 (23, 4)
Indiana	3.92 (80) / 0.47 (89)	0.214 (35, 5)	0.278 (35, 5)	0.560 (34, 5)	0.380 (19, 2)	0.276 (64, 9)
Penn State	3.49 (23) / 0.48 (92)	0.160 (41, 6)	0.262 (40, 6)	0.546 (43, 6)	0.301 (68, 6)	0.229 (33, 5)
Michigan	3.36 (15) / 0.42 (64)	-0.114 (76, 7)	0.177 (79, 8)	0.490 (77, 10)	0.351 (35, 3)	0.368 (106, 13)
Iowa	3.78 (59) / 0.43 (67)	-0.128 (78, 8)	0.180 (75, 7)	0.494 (74, 9)	0.284 (76, 8)	0.277 (65, 10)
Ohio State	3.1 (4) / 0.48 (92)	-0.200 (83, 9)	0.161 (82, 9)	0.519 (58, 7)	0.218 (111, 16)	0.230 (34, 6)
Wisconsin	3.77 (57) / 0.41 (61)	-0.244 (87, 10)	0.152 (86, 10)	0.363 (122, 16)	0.223 (106, 13)	0.230 (35, 7)
USC	3.3 (11) / 0.46 (84)	-0.265 (90, 11)	0.136 (93, 11)	0.477 (84, 11)	0.223 (107, 14)	0.276 (63, 8)
Nebraska	3.44 (19) / 0.39 (50)	-0.307 (98, 12)	0.132 (95, 12)	0.474 (86, 12)	0.294 (70, 7)	0.355 (101, 12)
Washington	3.53 (28) / 0.39 (50)	-0.463 (108, 13)	0.082 (108, 13)	0.356 (127, 17)	0.267 (87, 11)	0.508 (131, 18)
UCLA	3.93 (81) / 0.43 (67)	-0.471 (109, 14)	0.077 (110, 14)	0.506 (62, 8)	0.220 (108, 15)	0.284 (74, 11)
Michigan State	3.76 (55) / 0.45 (79)	-0.617 (117, 15)	0.042 (117, 15)	0.374 (119, 15)	0.185 (121, 17)	0.379 (111, 14)
Illinois	3.58 (31) / 0.46 (84)	-0.624 (118, 16)	0.039 (118, 16)	0.424 (108, 13)	0.254 (92, 12)	0.396 (119, 15)
Purdue	3.52 (26) / 0.4 (53)	-0.802 (125, 17)	-0.011 (126, 17)	0.339 (130, 18)	0.157 (126, 18)	0.430 (124, 16)
Minnesota	3.7 (47) / 0.44 (72)	-0.853 (127, 18)	-0.023 (127, 18)	0.407 (112, 14)	0.267 (86, 10)	0.433 (125, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Max Speed 10yds 20yds | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Ohio State	3.1 (4) / 0.48 (92)	19.576 (2, 1)	19.787 (1, 1)	0.623 (7, 2)	0.419 (4, 2)	0.197 (9, 2)
Oregon	3.54 (29) / 0.5 (97)	19.537 (6, 2)	19.703 (6, 2)	0.635 (6, 1)	0.427 (3, 1)	0.190 (8, 1)
Maryland	2.98 (2) / 0.37 (43)	19.399 (15, 3)	19.570 (18, 4)	0.580 (16, 3)	0.341 (29, 5)	0.216 (18, 3)
Penn State	3.49 (23) / 0.48 (92)	19.361 (21, 4)	19.551 (21, 5)	0.557 (34, 5)	0.369 (18, 4)	0.277 (47, 4)
Northwestern	3.86 (72) / 0.42 (64)	19.308 (30, 5)	19.651 (10, 3)	0.515 (55, 7)	0.335 (34, 7)	0.293 (65, 7)
Wisconsin	3.77 (57) / 0.41 (61)	19.160 (54, 6)	19.415 (53, 6)	0.562 (32, 4)	0.336 (32, 6)	0.295 (68, 8)
Rutgers	3.78 (59) / 0.47 (89)	19.125 (65, 7)	19.311 (80, 8)	0.552 (37, 6)	0.405 (5, 3)	0.347 (113, 14)
Iowa	3.78 (59) / 0.43 (67)	19.104 (73, 8)	19.401 (57, 7)	0.507 (61, 8)	0.287 (61, 8)	0.296 (72, 10)
Illinois	3.58 (31) / 0.46 (84)	19.091 (77, 9)	19.285 (88, 10)	0.494 (71, 9)	0.250 (91, 10)	0.282 (52, 5)
Nebraska	3.44 (19) / 0.39 (50)	19.030 (86, 10)	19.253 (97, 13)	0.468 (91, 11)	0.222 (110, 13)	0.296 (71, 9)
UCLA	3.93 (81) / 0.43 (67)	19.015 (91, 11)	19.218 (106, 14)	0.484 (77, 10)	0.284 (64, 9)	0.332 (103, 12)
Minnesota	3.7 (47) / 0.44 (72)	18.994 (96, 12)	19.276 (93, 12)	0.431 (114, 14)	0.197 (123, 15)	0.290 (61, 6)
Michigan	3.36 (15) / 0.42 (64)	18.964 (106, 13)	19.304 (81, 9)	0.425 (118, 15)	0.197 (122, 14)	0.312 (86, 11)
Indiana	3.92 (80) / 0.47 (89)	18.911 (114, 14)	19.282 (89, 11)	0.432 (113, 13)	0.227 (100, 12)	0.340 (111, 13)
Washington	3.53 (28) / 0.39 (50)	18.827 (123, 15)	19.125 (123, 15)	0.447 (105, 12)	0.227 (99, 11)	0.385 (123, 16)
Purdue	3.52 (26) / 0.4 (53)	18.740 (128, 16)	19.005 (129, 16)	0.380 (128, 16)	0.196 (124, 16)	0.407 (130, 17)
USC	3.3 (11) / 0.46 (84)	18.699 (131, 17)	19.001 (130, 17)	0.351 (132, 18)	0.178 (130, 17)	0.356 (116, 15)
Michigan State	3.76 (55) / 0.45 (79)	18.627 (132, 18)	18.893 (132, 18)	0.358 (130, 17)	0.152 (132, 18)	0.423 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Max Speed 10yds 20yds | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Ohio State	3.1 (4) / 0.48 (92)	0.300 (6, 1)	0.431 (6, 1)	0.595 (10, 1)	0.344 (22, 3)	0.180 (4, 1)
Maryland	2.98 (2) / 0.37 (43)	0.252 (16, 2)	0.380 (17, 4)	0.559 (24, 3)	0.368 (13, 2)	0.255 (40, 5)
Oregon	3.54 (29) / 0.5 (97)	0.240 (18, 3)	0.382 (15, 3)	0.536 (38, 6)	0.329 (32, 5)	0.220 (15, 2)
Northwestern	3.86 (72) / 0.42 (64)	0.234 (19, 4)	0.399 (9, 2)	0.545 (32, 4)	0.380 (10, 1)	0.305 (77, 10)
Penn State	3.49 (23) / 0.48 (92)	0.132 (53, 5)	0.298 (60, 7)	0.514 (56, 7)	0.292 (67, 9)	0.283 (63, 8)
Rutgers	3.78 (59) / 0.47 (89)	0.113 (62, 6)	0.277 (76, 10)	0.565 (22, 2)	0.342 (24, 4)	0.270 (53, 7)
Illinois	3.58 (31) / 0.46 (84)	0.106 (63, 7)	0.304 (55, 5)	0.479 (83, 12)	0.272 (84, 11)	0.229 (23, 3)
Wisconsin	3.77 (57) / 0.41 (61)	0.096 (69, 8)	0.300 (58, 6)	0.542 (34, 5)	0.313 (48, 6)	0.294 (68, 9)
Iowa	3.78 (59) / 0.43 (67)	0.082 (75, 9)	0.285 (70, 9)	0.481 (81, 11)	0.235 (112, 14)	0.243 (30, 4)
Washington	3.53 (28) / 0.39 (50)	0.073 (77, 10)	0.266 (84, 12)	0.507 (61, 8)	0.311 (50, 7)	0.317 (87, 12)
Minnesota	3.7 (47) / 0.44 (72)	0.072 (78, 11)	0.289 (66, 8)	0.460 (95, 13)	0.198 (130, 17)	0.268 (51, 6)
Michigan	3.36 (15) / 0.42 (64)	0.070 (80, 12)	0.272 (79, 11)	0.500 (65, 9)	0.305 (56, 8)	0.309 (80, 11)
Nebraska	3.44 (19) / 0.39 (50)	0.044 (93, 13)	0.225 (104, 15)	0.488 (76, 10)	0.290 (69, 10)	0.331 (100, 13)
Indiana	3.92 (80) / 0.47 (89)	-0.019 (111, 15)	0.247 (90, 13)	0.412 (124, 15)	0.240 (108, 13)	0.348 (109, 14)
UCLA	3.93 (81) / 0.43 (67)	-0.019 (110, 14)	0.226 (103, 14)	0.425 (117, 14)	0.243 (105, 12)	0.349 (111, 15)
USC	3.3 (11) / 0.46 (84)	-0.106 (130, 16)	0.164 (129, 16)	0.384 (130, 17)	0.224 (118, 15)	0.407 (132, 18)
Purdue	3.52 (26) / 0.4 (53)	-0.126 (132, 17)	0.136 (132, 17)	0.404 (127, 16)	0.211 (123, 16)	0.398 (130, 17)
Michigan State	3.76 (55) / 0.45 (79)	-0.146 (133, 18)	0.098 (134, 18)	0.357 (133, 18)	0.180 (134, 18)	0.367 (122, 16)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Max Speed 10yds 20yds | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Washington	3.53 (28) / 0.39 (50)	0.147 (17, 1)	0.103 (18, 1)	0.599 (10, 1)	0.372 (16, 1)	0.194 (13, 1)
Maryland	2.98 (2) / 0.37 (43)	0.041 (31, 2)	0.077 (27, 2)	0.540 (41, 2)	0.362 (23, 2)	0.282 (62, 7)
Nebraska	3.44 (19) / 0.39 (50)	0.010 (36, 3)	0.005 (43, 5)	0.517 (53, 6)	0.311 (46, 3)	0.253 (41, 6)
Ohio State	3.1 (4) / 0.48 (92)	-0.048 (44, 4)	0.013 (42, 4)	0.539 (42, 3)	0.277 (62, 5)	0.207 (17, 2)
Minnesota	3.7 (47) / 0.44 (72)	-0.066 (47, 5)	0.016 (41, 3)	0.514 (58, 7)	0.255 (81, 8)	0.234 (30, 5)
Michigan	3.36 (15) / 0.42 (64)	-0.125 (59, 6)	-0.029 (57, 6)	0.524 (49, 4)	0.255 (82, 9)	0.223 (25, 4)
Iowa	3.78 (59) / 0.43 (67)	-0.180 (69, 7)	-0.058 (73, 7)	0.518 (51, 5)	0.188 (124, 18)	0.222 (24, 3)
Northwestern	3.86 (72) / 0.42 (64)	-0.191 (75, 8)	-0.060 (74, 8)	0.466 (80, 10)	0.215 (108, 15)	0.285 (67, 8)
Wisconsin	3.77 (57) / 0.41 (61)	-0.198 (79, 9)	-0.070 (81, 10)	0.510 (60, 8)	0.290 (54, 4)	0.285 (69, 9)
Indiana	3.92 (80) / 0.47 (89)	-0.240 (84, 10)	-0.098 (90, 12)	0.441 (103, 14)	0.275 (65, 6)	0.333 (106, 15)
Illinois	3.58 (31) / 0.46 (84)	-0.252 (86, 11)	-0.066 (77, 9)	0.428 (109, 17)	0.239 (96, 12)	0.297 (80, 10)
Rutgers	3.78 (59) / 0.47 (89)	-0.280 (90, 12)	-0.106 (94, 14)	0.482 (70, 9)	0.250 (88, 10)	0.302 (84, 11)
Oregon	3.54 (29) / 0.5 (97)	-0.290 (94, 13)	-0.095 (89, 11)	0.449 (96, 12)	0.250 (89, 11)	0.334 (108, 16)
Michigan State	3.76 (55) / 0.45 (79)	-0.301 (100, 14)	-0.142 (112, 15)	0.454 (89, 11)	0.205 (114, 17)	0.306 (86, 12)
Penn State	3.49 (23) / 0.48 (92)	-0.339 (107, 15)	-0.102 (91, 13)	0.439 (104, 15)	0.229 (101, 13)	0.326 (97, 13)
Purdue	3.52 (26) / 0.4 (53)	-0.344 (109, 16)	-0.149 (114, 16)	0.443 (99, 13)	0.259 (77, 7)	0.354 (115, 17)
UCLA	3.93 (81) / 0.43 (67)	-0.427 (123, 17)	-0.194 (128, 17)	0.434 (105, 16)	0.207 (113, 16)	0.328 (99, 14)
USC	3.3 (11) / 0.46 (84)	-0.471 (128, 18)	-0.205 (130, 18)	0.419 (114, 18)	0.222 (104, 14)	0.382 (126, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Max Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Ohio State	3.1 (4) / 0.48 (92)	20.331 (3, 1)	20.264 (2, 1)	0.534 (39, 4)	0.330 (9, 2)	0.271 (32, 3)
Northwestern	3.86 (72) / 0.42 (64)	20.177 (21, 2)	20.237 (4, 2)	0.594 (8, 1)	0.332 (6, 1)	0.323 (72, 10)
Penn State	3.49 (23) / 0.48 (92)	20.159 (27, 3)	20.054 (37, 5)	0.522 (48, 6)	0.276 (39, 6)	0.265 (28, 2)
Oregon	3.54 (29) / 0.5 (97)	20.142 (31, 4)	20.081 (28, 3)	0.533 (40, 5)	0.286 (30, 5)	0.247 (13, 1)
Iowa	3.78 (59) / 0.43 (67)	20.094 (42, 5)	20.079 (29, 4)	0.544 (28, 2)	0.299 (25, 4)	0.296 (56, 6)
Wisconsin	3.77 (57) / 0.41 (61)	20.074 (48, 6)	20.009 (50, 7)	0.489 (69, 8)	0.228 (86, 8)	0.274 (35, 4)
Indiana	3.92 (80) / 0.47 (89)	20.006 (66, 7)	20.029 (46, 6)	0.495 (64, 7)	0.261 (49, 7)	0.313 (67, 8)
Rutgers	3.78 (59) / 0.47 (89)	19.994 (69, 8)	19.898 (80, 9)	0.540 (35, 3)	0.324 (14, 3)	0.301 (59, 7)
Minnesota	3.7 (47) / 0.44 (72)	19.956 (76, 9)	19.920 (74, 8)	0.438 (98, 11)	0.195 (113, 13)	0.322 (71, 9)
Maryland	2.98 (2) / 0.37 (43)	19.948 (78, 10)	19.886 (87, 10)	0.439 (97, 10)	0.218 (94, 10)	0.342 (90, 12)
Nebraska	3.44 (19) / 0.39 (50)	19.876 (91, 11)	19.858 (94, 11)	0.417 (114, 13)	0.156 (126, 15)	0.296 (55, 5)
Illinois	3.58 (31) / 0.46 (84)	19.841 (99, 12)	19.773 (108, 13)	0.458 (87, 9)	0.222 (89, 9)	0.337 (84, 11)
UCLA	3.93 (81) / 0.43 (67)	19.772 (110, 13)	19.700 (120, 14)	0.433 (101, 12)	0.217 (96, 11)	0.381 (110, 13)
Michigan	3.36 (15) / 0.42 (64)	19.709 (118, 14)	19.806 (102, 12)	0.351 (129, 16)	0.141 (131, 16)	0.404 (121, 14)
Washington	3.53 (28) / 0.39 (50)	19.622 (126, 15)	19.666 (122, 15)	0.388 (126, 14)	0.216 (97, 12)	0.435 (129, 16)
USC	3.3 (11) / 0.46 (84)	19.585 (130, 16)	19.628 (125, 16)	0.374 (127, 15)	0.172 (122, 14)	0.421 (128, 15)
Purdue	3.52 (26) / 0.4 (53)	19.494 (131, 17)	19.473 (132, 17)	0.308 (133, 18)	0.137 (133, 18)	0.458 (131, 17)
Michigan State	3.76 (55) / 0.45 (79)	19.421 (132, 18)	19.466 (133, 18)	0.312 (132, 17)	0.137 (132, 17)	0.477 (133, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Max Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.264 (13, 1)	0.295 (5, 1)	0.593 (8, 1)	0.342 (7, 1)	0.283 (48, 8)
Ohio State	3.1 (4) / 0.48 (92)	0.205 (22, 2)	0.250 (19, 2)	0.494 (55, 7)	0.296 (31, 2)	0.254 (22, 2)
Iowa	3.78 (59) / 0.43 (67)	0.148 (38, 3)	0.214 (33, 3)	0.520 (35, 2)	0.277 (41, 3)	0.276 (40, 6)
Rutgers	3.78 (59) / 0.47 (89)	0.138 (40, 4)	0.188 (53, 8)	0.518 (36, 3)	0.271 (49, 4)	0.266 (33, 3)
Oregon	3.54 (29) / 0.5 (97)	0.131 (44, 5)	0.202 (41, 5)	0.506 (45, 5)	0.253 (66, 7)	0.318 (81, 11)
Penn State	3.49 (23) / 0.48 (92)	0.128 (46, 6)	0.193 (49, 7)	0.509 (43, 4)	0.260 (58, 6)	0.272 (35, 4)
Minnesota	3.7 (47) / 0.44 (72)	0.121 (50, 7)	0.206 (38, 4)	0.467 (81, 9)	0.180 (129, 17)	0.248 (19, 1)
Indiana	3.92 (80) / 0.47 (89)	0.099 (57, 8)	0.197 (46, 6)	0.492 (56, 8)	0.234 (91, 9)	0.276 (39, 5)
Wisconsin	3.77 (57) / 0.41 (61)	0.097 (58, 9)	0.182 (61, 10)	0.440 (108, 12)	0.242 (86, 8)	0.279 (43, 7)
Illinois	3.58 (31) / 0.46 (84)	0.090 (65, 10)	0.185 (56, 9)	0.501 (48, 6)	0.267 (52, 5)	0.298 (59, 9)
Nebraska	3.44 (19) / 0.39 (50)	0.026 (97, 11)	0.140 (103, 13)	0.380 (131, 16)	0.198 (120, 15)	0.305 (68, 10)
Washington	3.53 (28) / 0.39 (50)	0.025 (99, 12)	0.144 (97, 11)	0.464 (85, 10)	0.224 (100, 11)	0.319 (82, 12)
Maryland	2.98 (2) / 0.37 (43)	0.012 (102, 13)	0.138 (104, 14)	0.425 (118, 14)	0.202 (116, 13)	0.342 (103, 15)
Michigan	3.36 (15) / 0.42 (64)	0.011 (103, 14)	0.141 (102, 12)	0.447 (101, 11)	0.233 (92, 10)	0.338 (99, 13)
UCLA	3.93 (81) / 0.43 (67)	-0.008 (109, 15)	0.128 (110, 15)	0.436 (110, 13)	0.201 (118, 14)	0.340 (100, 14)
USC	3.3 (11) / 0.46 (84)	-0.088 (125, 16)	0.103 (123, 16)	0.393 (130, 15)	0.221 (103, 12)	0.431 (132, 16)
Purdue	3.52 (26) / 0.4 (53)	-0.198 (133, 17)	0.029 (134, 18)	0.361 (134, 18)	0.176 (132, 18)	0.447 (134, 18)
Michigan State	3.76 (55) / 0.45 (79)	-0.199 (134, 18)	0.029 (133, 17)	0.368 (133, 17)	0.198 (121, 16)	0.438 (133, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Max Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Nebraska	3.44 (19) / 0.39 (50)	0.163 (17, 1)	0.163 (24, 1)	0.591 (12, 1)	0.315 (40, 2)	0.140 (3, 2)
Northwestern	3.86 (72) / 0.42 (64)	-0.006 (36, 2)	0.133 (32, 2)	0.505 (58, 6)	0.328 (34, 1)	0.312 (93, 13)
Ohio State	3.1 (4) / 0.48 (92)	-0.031 (39, 3)	0.104 (45, 4)	0.551 (27, 2)	0.312 (43, 3)	0.254 (39, 7)
Minnesota	3.7 (47) / 0.44 (72)	-0.033 (41, 4)	0.118 (37, 3)	0.490 (71, 10)	0.206 (113, 14)	0.139 (2, 1)
Iowa	3.78 (59) / 0.43 (67)	-0.069 (48, 5)	0.087 (54, 6)	0.530 (36, 3)	0.251 (83, 9)	0.232 (23, 3)
Washington	3.53 (28) / 0.39 (50)	-0.088 (56, 6)	0.095 (49, 5)	0.508 (52, 4)	0.276 (67, 7)	0.275 (50, 8)
Indiana	3.92 (80) / 0.47 (89)	-0.147 (68, 7)	0.039 (82, 10)	0.507 (54, 5)	0.277 (66, 6)	0.277 (53, 9)
Rutgers	3.78 (59) / 0.47 (89)	-0.151 (69, 8)	0.046 (78, 9)	0.505 (60, 7)	0.247 (88, 10)	0.234 (25, 4)
Michigan	3.36 (15) / 0.42 (64)	-0.165 (72, 9)	0.057 (72, 7)	0.471 (82, 11)	0.224 (101, 13)	0.247 (33, 6)
Illinois	3.58 (31) / 0.46 (84)	-0.168 (75, 10)	0.049 (75, 8)	0.494 (67, 9)	0.289 (59, 5)	0.308 (83, 11)
Wisconsin	3.77 (57) / 0.41 (61)	-0.206 (84, 11)	0.021 (94, 13)	0.497 (66, 8)	0.309 (44, 4)	0.336 (110, 16)
UCLA	3.93 (81) / 0.43 (67)	-0.237 (93, 12)	-0.010 (111, 15)	0.442 (109, 13)	0.236 (92, 12)	0.308 (84, 12)
Maryland	2.98 (2) / 0.37 (43)	-0.262 (99, 13)	0.028 (88, 12)	0.467 (87, 12)	0.247 (89, 11)	0.315 (95, 14)
Penn State	3.49 (23) / 0.48 (92)	-0.267 (101, 14)	0.036 (83, 11)	0.413 (120, 17)	0.199 (116, 16)	0.293 (71, 10)
Oregon	3.54 (29) / 0.5 (97)	-0.294 (107, 15)	0.008 (102, 14)	0.372 (130, 18)	0.140 (134, 18)	0.243 (28, 5)
Michigan State	3.76 (55) / 0.45 (79)	-0.368 (120, 16)	-0.056 (125, 17)	0.416 (116, 16)	0.194 (120, 17)	0.343 (114, 17)
USC	3.3 (11) / 0.46 (84)	-0.369 (121, 17)	-0.036 (122, 16)	0.433 (110, 14)	0.202 (114, 15)	0.330 (106, 15)
Purdue	3.52 (26) / 0.4 (53)	-0.389 (124, 18)	-0.059 (126, 18)	0.430 (112, 15)	0.265 (72, 8)	0.404 (133, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Top 3 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Ohio State	3.1 (4) / 0.48 (92)	19.870 (2, 1)	19.799 (1, 1)	0.557 (25, 3)	0.342 (16, 3)	0.249 (19, 1)
Oregon	3.54 (29) / 0.5 (97)	19.683 (21, 2)	19.586 (24, 3)	0.537 (41, 5)	0.308 (28, 5)	0.260 (25, 2)
Northwestern	3.86 (72) / 0.42 (64)	19.651 (26, 3)	19.690 (9, 2)	0.579 (13, 1)	0.343 (14, 2)	0.309 (72, 10)
Penn State	3.49 (23) / 0.48 (92)	19.645 (29, 4)	19.552 (34, 4)	0.508 (54, 6)	0.293 (36, 6)	0.283 (41, 3)
Maryland	2.98 (2) / 0.37 (43)	19.563 (49, 5)	19.490 (52, 7)	0.488 (73, 8)	0.264 (56, 8)	0.298 (59, 6)
Iowa	3.78 (59) / 0.43 (67)	19.554 (52, 6)	19.547 (37, 5)	0.547 (33, 4)	0.310 (26, 4)	0.285 (44, 4)
Wisconsin	3.77 (57) / 0.41 (61)	19.541 (57, 7)	19.493 (51, 6)	0.499 (64, 7)	0.265 (55, 7)	0.304 (65, 8)
Rutgers	3.78 (59) / 0.47 (89)	19.529 (61, 8)	19.429 (71, 8)	0.560 (23, 2)	0.381 (2, 1)	0.307 (67, 9)
Minnesota	3.7 (47) / 0.44 (72)	19.453 (74, 9)	19.414 (75, 9)	0.433 (103, 13)	0.186 (120, 14)	0.303 (63, 7)
Nebraska	3.44 (19) / 0.39 (50)	19.427 (79, 10)	19.406 (78, 10)	0.458 (88, 10)	0.194 (115, 13)	0.291 (53, 5)
Indiana	3.92 (80) / 0.47 (89)	19.348 (94, 11)	19.400 (82, 11)	0.469 (85, 9)	0.248 (72, 9)	0.320 (79, 11)
Illinois	3.58 (31) / 0.46 (84)	19.337 (96, 12)	19.255 (109, 13)	0.450 (92, 11)	0.202 (111, 12)	0.338 (94, 12)
Michigan	3.36 (15) / 0.42 (64)	19.287 (107, 13)	19.374 (90, 12)	0.399 (120, 15)	0.174 (124, 15)	0.351 (102, 14)
UCLA	3.93 (81) / 0.43 (67)	19.266 (110, 14)	19.193 (117, 14)	0.443 (96, 12)	0.209 (107, 11)	0.342 (96, 13)
Washington	3.53 (28) / 0.39 (50)	19.099 (125, 15)	19.137 (122, 15)	0.402 (118, 14)	0.226 (92, 10)	0.421 (128, 16)
Purdue	3.52 (26) / 0.4 (53)	19.053 (128, 16)	19.036 (130, 16)	0.349 (130, 16)	0.171 (125, 16)	0.424 (129, 17)
USC	3.3 (11) / 0.46 (84)	18.992 (131, 17)	19.034 (131, 17)	0.345 (131, 17)	0.133 (133, 18)	0.398 (121, 15)
Michigan State	3.76 (55) / 0.45 (79)	18.942 (132, 18)	18.956 (132, 18)	0.329 (132, 18)	0.138 (132, 17)	0.469 (133, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Top 3 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.245 (12, 1)	0.318 (9, 1)	0.530 (33, 2)	0.337 (14, 1)	0.299 (66, 8)
Ohio State	3.1 (4) / 0.48 (92)	0.206 (21, 2)	0.292 (15, 2)	0.507 (50, 5)	0.294 (34, 3)	0.252 (28, 4)
Rutgers	3.78 (59) / 0.47 (89)	0.163 (26, 3)	0.229 (48, 6)	0.544 (23, 1)	0.280 (42, 4)	0.237 (15, 1)
Iowa	3.78 (59) / 0.43 (67)	0.126 (44, 4)	0.234 (40, 3)	0.522 (38, 3)	0.258 (65, 8)	0.264 (32, 5)
Oregon	3.54 (29) / 0.5 (97)	0.123 (47, 5)	0.231 (44, 5)	0.488 (65, 6)	0.295 (32, 2)	0.323 (89, 12)
Minnesota	3.7 (47) / 0.44 (72)	0.105 (53, 6)	0.234 (41, 4)	0.428 (116, 14)	0.177 (132, 17)	0.245 (22, 3)
Maryland	2.98 (2) / 0.37 (43)	0.094 (57, 8)	0.214 (60, 8)	0.483 (72, 9)	0.261 (62, 7)	0.309 (78, 10)
Penn State	3.49 (23) / 0.48 (92)	0.094 (56, 7)	0.208 (63, 9)	0.487 (67, 7)	0.237 (88, 9)	0.292 (58, 6)
Illinois	3.58 (31) / 0.46 (84)	0.066 (72, 9)	0.215 (59, 7)	0.474 (81, 10)	0.228 (101, 12)	0.294 (59, 7)
Michigan	3.36 (15) / 0.42 (64)	0.060 (77, 10)	0.191 (80, 11)	0.508 (49, 4)	0.266 (60, 6)	0.304 (73, 9)
Nebraska	3.44 (19) / 0.39 (50)	0.051 (82, 11)	0.184 (91, 12)	0.445 (107, 12)	0.195 (122, 14)	0.237 (16, 2)
Wisconsin	3.77 (57) / 0.41 (61)	0.050 (83, 12)	0.197 (73, 10)	0.463 (91, 11)	0.273 (54, 5)	0.342 (107, 13)
Washington	3.53 (28) / 0.39 (50)	0.015 (102, 13)	0.168 (99, 13)	0.485 (68, 8)	0.235 (90, 11)	0.322 (88, 11)
Indiana	3.92 (80) / 0.47 (89)	-0.027 (114, 14)	0.165 (103, 14)	0.433 (113, 13)	0.237 (89, 10)	0.367 (119, 15)
UCLA	3.93 (81) / 0.43 (67)	-0.052 (118, 15)	0.136 (118, 15)	0.406 (123, 15)	0.185 (129, 16)	0.352 (115, 14)
USC	3.3 (11) / 0.46 (84)	-0.107 (131, 16)	0.111 (126, 16)	0.359 (133, 17)	0.185 (128, 15)	0.413 (132, 16)
Purdue	3.52 (26) / 0.4 (53)	-0.158 (133, 17)	0.072 (133, 17)	0.374 (132, 16)	0.209 (114, 13)	0.447 (134, 18)
Michigan State	3.76 (55) / 0.45 (79)	-0.206 (134, 18)	0.036 (134, 18)	0.330 (134, 18)	0.157 (133, 18)	0.427 (133, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Top 3 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Nebraska	3.44 (19) / 0.39 (50)	0.195 (13, 1)	0.214 (18, 1)	0.625 (8, 1)	0.337 (24, 1)	0.139 (3, 1)
Ohio State	3.1 (4) / 0.48 (92)	-0.025 (34, 2)	0.119 (36, 4)	0.537 (34, 2)	0.307 (39, 2)	0.239 (37, 6)
Maryland	2.98 (2) / 0.37 (43)	-0.040 (36, 3)	0.145 (30, 2)	0.536 (35, 3)	0.302 (45, 4)	0.251 (43, 8)
Washington	3.53 (28) / 0.39 (50)	-0.078 (41, 4)	0.119 (35, 3)	0.532 (38, 4)	0.303 (44, 3)	0.248 (40, 7)
Northwestern	3.86 (72) / 0.42 (64)	-0.080 (42, 5)	0.098 (42, 5)	0.486 (72, 9)	0.292 (55, 5)	0.299 (88, 11)
Iowa	3.78 (59) / 0.43 (67)	-0.100 (47, 6)	0.074 (57, 7)	0.497 (58, 6)	0.268 (63, 7)	0.211 (21, 3)
Rutgers	3.78 (59) / 0.47 (89)	-0.146 (62, 7)	0.043 (70, 9)	0.527 (42, 5)	0.254 (73, 8)	0.231 (34, 5)
Minnesota	3.7 (47) / 0.44 (72)	-0.154 (65, 8)	0.078 (54, 6)	0.492 (64, 7)	0.194 (112, 14)	0.189 (13, 2)
Michigan	3.36 (15) / 0.42 (64)	-0.175 (68, 9)	0.064 (62, 8)	0.489 (69, 8)	0.212 (100, 12)	0.221 (27, 4)
Illinois	3.58 (31) / 0.46 (84)	-0.248 (83, 10)	0.023 (80, 10)	0.458 (93, 11)	0.247 (80, 9)	0.299 (89, 12)
Wisconsin	3.77 (57) / 0.41 (61)	-0.268 (87, 11)	-0.017 (96, 13)	0.453 (100, 13)	0.278 (58, 6)	0.343 (114, 15)
Oregon	3.54 (29) / 0.5 (97)	-0.278 (88, 12)	0.022 (81, 11)	0.431 (112, 15)	0.204 (104, 13)	0.306 (97, 13)
Penn State	3.49 (23) / 0.48 (92)	-0.295 (91, 13)	0.020 (82, 12)	0.453 (99, 12)	0.190 (117, 15)	0.270 (59, 9)
Indiana	3.92 (80) / 0.47 (89)	-0.330 (99, 14)	-0.067 (114, 14)	0.460 (92, 10)	0.223 (91, 10)	0.314 (100, 14)
UCLA	3.93 (81) / 0.43 (67)	-0.375 (110, 15)	-0.088 (120, 15)	0.434 (110, 14)	0.180 (122, 16)	0.275 (66, 10)
Purdue	3.52 (26) / 0.4 (53)	-0.493 (125, 16)	-0.140 (128, 17)	0.398 (119, 16)	0.222 (92, 11)	0.391 (131, 18)
USC	3.3 (11) / 0.46 (84)	-0.497 (126, 17)	-0.136 (125, 16)	0.385 (123, 17)	0.171 (127, 17)	0.353 (121, 17)
Michigan State	3.76 (55) / 0.45 (79)	-0.502 (127, 18)	-0.158 (133, 18)	0.382 (125, 18)	0.164 (130, 18)	0.345 (116, 16)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Top 5 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Ohio State	3.1 (4) / 0.48 (92)	19.578 (1, 1)	19.502 (1, 1)	0.585 (7, 1)	0.324 (19, 2)	0.198 (3, 1)
Oregon	3.54 (29) / 0.5 (97)	19.381 (17, 2)	19.268 (21, 3)	0.539 (38, 5)	0.321 (22, 3)	0.268 (35, 3)
Penn State	3.49 (23) / 0.48 (92)	19.315 (29, 3)	19.214 (36, 5)	0.508 (56, 7)	0.302 (36, 6)	0.291 (57, 5)
Northwestern	3.86 (72) / 0.42 (64)	19.314 (30, 4)	19.348 (11, 2)	0.561 (26, 2)	0.318 (24, 4)	0.305 (70, 9)
Iowa	3.78 (59) / 0.43 (67)	19.278 (41, 5)	19.264 (22, 4)	0.547 (35, 4)	0.310 (28, 5)	0.278 (43, 4)
Maryland	2.98 (2) / 0.37 (43)	19.264 (49, 6)	19.185 (48, 6)	0.480 (80, 8)	0.260 (60, 8)	0.300 (65, 6)
Rutgers	3.78 (59) / 0.47 (89)	19.226 (56, 7)	19.122 (65, 9)	0.560 (27, 3)	0.381 (3, 1)	0.308 (73, 10)
Wisconsin	3.77 (57) / 0.41 (61)	19.217 (59, 8)	19.167 (52, 7)	0.516 (53, 6)	0.285 (46, 7)	0.300 (66, 7)
Minnesota	3.7 (47) / 0.44 (72)	19.177 (68, 9)	19.130 (62, 8)	0.461 (87, 9)	0.197 (116, 14)	0.266 (33, 2)
Nebraska	3.44 (19) / 0.39 (50)	19.105 (80, 10)	19.080 (79, 10)	0.456 (93, 11)	0.197 (115, 13)	0.302 (68, 8)
Illinois	3.58 (31) / 0.46 (84)	19.036 (91, 11)	18.944 (103, 13)	0.456 (94, 12)	0.203 (110, 12)	0.329 (92, 14)
Michigan	3.36 (15) / 0.42 (64)	18.992 (99, 12)	19.066 (84, 11)	0.425 (110, 14)	0.188 (119, 15)	0.326 (88, 13)
Indiana	3.92 (80) / 0.47 (89)	18.960 (105, 13)	19.011 (93, 12)	0.460 (88, 10)	0.233 (88, 9)	0.313 (77, 11)
UCLA	3.93 (81) / 0.43 (67)	18.953 (108, 14)	18.876 (116, 14)	0.447 (99, 13)	0.205 (107, 11)	0.317 (82, 12)
Washington	3.53 (28) / 0.39 (50)	18.794 (122, 15)	18.829 (119, 15)	0.415 (114, 15)	0.229 (91, 10)	0.405 (126, 17)
Purdue	3.52 (26) / 0.4 (53)	18.758 (127, 16)	18.743 (125, 16)	0.366 (128, 16)	0.185 (121, 16)	0.399 (124, 16)
Michigan State	3.76 (55) / 0.45 (79)	18.642 (131, 17)	18.641 (132, 18)	0.332 (132, 18)	0.141 (131, 17)	0.467 (133, 18)
USC	3.3 (11) / 0.46 (84)	18.639 (132, 18)	18.685 (131, 17)	0.336 (131, 17)	0.128 (133, 18)	0.386 (119, 15)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Top 5 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.241 (15, 1)	0.348 (9, 1)	0.529 (31, 2)	0.334 (14, 1)	0.288 (61, 6)
Ohio State	3.1 (4) / 0.48 (92)	0.215 (20, 2)	0.326 (16, 2)	0.517 (45, 3)	0.285 (39, 5)	0.242 (22, 4)
Rutgers	3.78 (59) / 0.47 (89)	0.170 (26, 3)	0.258 (49, 6)	0.574 (12, 1)	0.296 (29, 3)	0.234 (18, 3)
Iowa	3.78 (59) / 0.43 (67)	0.151 (40, 4)	0.276 (33, 3)	0.516 (47, 4)	0.264 (68, 8)	0.259 (36, 5)
Oregon	3.54 (29) / 0.5 (97)	0.129 (47, 5)	0.264 (41, 5)	0.515 (48, 5)	0.325 (16, 2)	0.311 (88, 12)
Minnesota	3.7 (47) / 0.44 (72)	0.116 (50, 6)	0.271 (38, 4)	0.446 (106, 13)	0.177 (131, 16)	0.225 (14, 1)
Maryland	2.98 (2) / 0.37 (43)	0.113 (51, 7)	0.253 (51, 7)	0.492 (67, 7)	0.269 (64, 7)	0.301 (76, 9)
Michigan	3.36 (15) / 0.42 (64)	0.080 (65, 8)	0.226 (74, 10)	0.504 (57, 6)	0.286 (38, 4)	0.303 (79, 10)
Illinois	3.58 (31) / 0.46 (84)	0.079 (67, 9)	0.250 (54, 8)	0.475 (84, 9)	0.233 (97, 11)	0.291 (65, 8)
Penn State	3.49 (23) / 0.48 (92)	0.078 (68, 10)	0.225 (76, 11)	0.492 (68, 8)	0.244 (87, 9)	0.289 (63, 7)
Nebraska	3.44 (19) / 0.39 (50)	0.064 (77, 11)	0.217 (83, 12)	0.473 (86, 11)	0.198 (121, 14)	0.230 (15, 2)
Wisconsin	3.77 (57) / 0.41 (61)	0.051 (88, 12)	0.226 (73, 9)	0.466 (96, 12)	0.281 (48, 6)	0.354 (115, 14)
Washington	3.53 (28) / 0.39 (50)	0.049 (92, 13)	0.213 (90, 13)	0.474 (85, 10)	0.227 (106, 12)	0.309 (87, 11)
UCLA	3.93 (81) / 0.43 (67)	-0.070 (120, 14)	0.150 (120, 15)	0.413 (121, 15)	0.191 (126, 15)	0.348 (113, 13)
Indiana	3.92 (80) / 0.47 (89)	-0.078 (123, 15)	0.162 (115, 14)	0.434 (114, 14)	0.239 (90, 10)	0.365 (119, 15)
USC	3.3 (11) / 0.46 (84)	-0.118 (132, 16)	0.127 (127, 16)	0.323 (134, 18)	0.164 (132, 17)	0.416 (131, 16)
Purdue	3.52 (26) / 0.4 (53)	-0.153 (133, 17)	0.097 (133, 17)	0.392 (126, 16)	0.221 (113, 13)	0.426 (133, 18)
Michigan State	3.76 (55) / 0.45 (79)	-0.206 (134, 18)	0.055 (134, 18)	0.342 (133, 17)	0.161 (133, 18)	0.425 (132, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Top 5 Avg Speed | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Nebraska	3.44 (19) / 0.39 (50)	0.126 (18, 1)	0.208 (25, 1)	0.617 (13, 1)	0.329 (28, 1)	0.143 (5, 1)
Maryland	2.98 (2) / 0.37 (43)	-0.011 (32, 2)	0.200 (26, 2)	0.534 (40, 3)	0.320 (31, 2)	0.255 (62, 9)
Ohio State	3.1 (4) / 0.48 (92)	-0.050 (38, 3)	0.150 (39, 4)	0.542 (34, 2)	0.298 (44, 3)	0.222 (33, 4)
Washington	3.53 (28) / 0.39 (50)	-0.055 (39, 4)	0.166 (33, 3)	0.532 (41, 4)	0.294 (51, 4)	0.225 (35, 5)
Northwestern	3.86 (72) / 0.42 (64)	-0.100 (45, 5)	0.113 (47, 5)	0.498 (62, 7)	0.256 (71, 8)	0.240 (49, 7)
Iowa	3.78 (59) / 0.43 (67)	-0.121 (48, 6)	0.098 (53, 8)	0.508 (54, 6)	0.256 (69, 7)	0.197 (16, 3)
Rutgers	3.78 (59) / 0.47 (89)	-0.153 (54, 7)	0.069 (68, 9)	0.516 (50, 5)	0.257 (68, 6)	0.244 (52, 8)
Minnesota	3.7 (47) / 0.44 (72)	-0.166 (57, 8)	0.108 (48, 6)	0.497 (63, 8)	0.206 (102, 14)	0.188 (12, 2)
Michigan	3.36 (15) / 0.42 (64)	-0.167 (58, 9)	0.101 (50, 7)	0.481 (73, 9)	0.232 (84, 11)	0.227 (36, 6)
Illinois	3.58 (31) / 0.46 (84)	-0.267 (80, 10)	0.043 (77, 11)	0.450 (101, 13)	0.239 (79, 10)	0.267 (71, 12)
Penn State	3.49 (23) / 0.48 (92)	-0.298 (86, 11)	0.051 (75, 10)	0.453 (96, 10)	0.193 (110, 15)	0.262 (67, 10)
Wisconsin	3.77 (57) / 0.41 (61)	-0.299 (87, 12)	-0.009 (98, 13)	0.452 (98, 11)	0.280 (55, 5)	0.336 (114, 15)
Oregon	3.54 (29) / 0.5 (97)	-0.305 (88, 13)	0.036 (81, 12)	0.437 (106, 14)	0.246 (75, 9)	0.329 (112, 14)
UCLA	3.93 (81) / 0.43 (67)	-0.430 (114, 14)	-0.098 (119, 14)	0.419 (113, 15)	0.152 (130, 18)	0.263 (69, 11)
Indiana	3.92 (80) / 0.47 (89)	-0.439 (118, 15)	-0.109 (121, 15)	0.450 (99, 12)	0.211 (98, 12)	0.315 (105, 13)
Purdue	3.52 (26) / 0.4 (53)	-0.503 (121, 16)	-0.126 (125, 16)	0.387 (123, 16)	0.209 (100, 13)	0.349 (122, 17)
USC	3.3 (11) / 0.46 (84)	-0.547 (127, 17)	-0.153 (127, 17)	0.376 (127, 17)	0.153 (129, 17)	0.337 (116, 16)
Michigan State	3.76 (55) / 0.45 (79)	-0.576 (129, 18)	-0.182 (132, 18)	0.365 (130, 18)	0.154 (128, 16)	0.375 (128, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Game Speed 10 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	1.771 (7, 1)	1.768 (9, 2)	0.736 (2, 1)	0.412 (18, 3)	0.085 (2, 1)
Oregon	3.54 (29) / 0.5 (97)	1.773 (9, 2)	1.757 (3, 1)	0.654 (6, 2)	0.464 (6, 1)	0.216 (22, 2)
Ohio State	3.1 (4) / 0.48 (92)	1.777 (14, 3)	1.773 (20, 3)	0.602 (21, 4)	0.425 (11, 2)	0.241 (36, 4)
Iowa	3.78 (59) / 0.43 (67)	1.788 (38, 4)	1.790 (85, 7)	0.560 (36, 5)	0.368 (29, 4)	0.255 (45, 6)
Penn State	3.49 (23) / 0.48 (92)	1.789 (42, 5)	1.778 (33, 4)	0.622 (15, 3)	0.283 (86, 9)	0.249 (42, 5)
Minnesota	3.7 (47) / 0.44 (72)	1.795 (68, 6)	1.783 (59, 5)	0.540 (46, 6)	0.260 (95, 10)	0.240 (34, 3)
Nebraska	3.44 (19) / 0.39 (50)	1.800 (78, 7)	1.786 (70, 6)	0.477 (80, 8)	0.320 (60, 5)	0.360 (104, 10)
Maryland	2.98 (2) / 0.37 (43)	1.804 (92, 8)	1.795 (104, 11)	0.416 (112, 13)	0.249 (98, 12)	0.351 (97, 8)
Rutgers	3.78 (59) / 0.47 (89)	1.804 (93, 9)	1.801 (121, 16)	0.490 (75, 7)	0.316 (65, 6)	0.355 (102, 9)
Indiana	3.92 (80) / 0.47 (89)	1.806 (104, 10)	1.797 (113, 12)	0.432 (103, 12)	0.292 (79, 8)	0.410 (126, 15)
Washington	3.53 (28) / 0.39 (50)	1.807 (106, 11)	1.790 (87, 8)	0.452 (94, 10)	0.297 (76, 7)	0.383 (116, 12)
Wisconsin	3.77 (57) / 0.41 (61)	1.807 (108, 12)	1.795 (101, 10)	0.439 (98, 11)	0.259 (96, 11)	0.363 (105, 11)
Purdue	3.52 (26) / 0.4 (53)	1.808 (110, 13)	1.797 (114, 13)	0.454 (93, 9)	0.227 (110, 14)	0.316 (79, 7)
Illinois	3.58 (31) / 0.46 (84)	1.815 (124, 14)	1.801 (120, 15)	0.386 (125, 16)	0.235 (106, 13)	0.436 (132, 18)
Michigan State	3.76 (55) / 0.45 (79)	1.817 (130, 16)	1.798 (118, 14)	0.387 (124, 15)	0.215 (117, 15)	0.421 (128, 16)
UCLA	3.93 (81) / 0.43 (67)	1.817 (128, 15)	1.805 (130, 17)	0.395 (120, 14)	0.213 (120, 16)	0.401 (124, 14)
Michigan	3.36 (15) / 0.42 (64)	1.818 (131, 17)	1.794 (95, 9)	0.344 (130, 18)	0.149 (132, 18)	0.399 (123, 13)
USC	3.3 (11) / 0.46 (84)	1.820 (132, 18)	1.807 (131, 18)	0.378 (126, 17)	0.188 (128, 17)	0.423 (129, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Game Speed 10 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	-0.013 (2, 1)	-0.006 (2, 1)	0.740 (1, 1)	0.449 (8, 3)	0.063 (1, 1)
Oregon	3.54 (29) / 0.5 (97)	-0.012 (3, 2)	-0.006 (3, 2)	0.676 (4, 2)	0.489 (3, 1)	0.178 (8, 3)
Ohio State	3.1 (4) / 0.48 (92)	-0.007 (13, 3)	-0.003 (14, 3)	0.599 (21, 3)	0.453 (6, 2)	0.270 (47, 5)
Penn State	3.49 (23) / 0.48 (92)	-0.004 (30, 4)	-0.003 (28, 4)	0.576 (30, 5)	0.305 (65, 8)	0.208 (17, 4)
Minnesota	3.7 (47) / 0.44 (72)	-0.003 (40, 5)	-0.002 (32, 5)	0.518 (55, 7)	0.316 (54, 7)	0.276 (55, 6)
Michigan	3.36 (15) / 0.42 (64)	-0.001 (67, 7)	-0.001 (64, 6)	0.580 (28, 4)	0.211 (112, 15)	0.176 (5, 2)
Nebraska	3.44 (19) / 0.39 (50)	-0.001 (65, 6)	-0.001 (66, 8)	0.519 (54, 6)	0.350 (39, 4)	0.320 (78, 8)
Washington	3.53 (28) / 0.39 (50)	-0.000 (68, 8)	-0.001 (65, 7)	0.501 (68, 8)	0.285 (74, 11)	0.278 (56, 7)
Wisconsin	3.77 (57) / 0.41 (61)	0.001 (72, 9)	-0.000 (73, 9)	0.465 (88, 10)	0.336 (43, 5)	0.381 (114, 15)
Iowa	3.78 (59) / 0.43 (67)	0.002 (80, 10)	0.000 (84, 10)	0.495 (70, 9)	0.319 (52, 6)	0.335 (89, 9)
Illinois	3.58 (31) / 0.46 (84)	0.004 (98, 11)	0.001 (96, 12)	0.465 (89, 11)	0.293 (70, 10)	0.360 (105, 12)
Indiana	3.92 (80) / 0.47 (89)	0.004 (99, 12)	0.001 (94, 11)	0.463 (90, 12)	0.296 (69, 9)	0.363 (107, 14)
Rutgers	3.78 (59) / 0.47 (89)	0.004 (102, 13)	0.001 (100, 13)	0.395 (118, 17)	0.203 (116, 16)	0.343 (97, 10)
Purdue	3.52 (26) / 0.4 (53)	0.005 (108, 14)	0.001 (110, 14)	0.426 (105, 13)	0.231 (106, 12)	0.361 (106, 13)
Maryland	2.98 (2) / 0.37 (43)	0.006 (110, 15)	0.002 (112, 15)	0.422 (110, 14)	0.222 (109, 14)	0.351 (102, 11)
UCLA	3.93 (81) / 0.43 (67)	0.007 (117, 16)	0.002 (115, 16)	0.401 (116, 15)	0.225 (108, 13)	0.396 (118, 16)
Michigan State	3.76 (55) / 0.45 (79)	0.009 (124, 17)	0.003 (125, 17)	0.397 (117, 16)	0.197 (121, 17)	0.414 (121, 17)
USC	3.3 (11) / 0.46 (84)	0.011 (130, 18)	0.004 (131, 18)	0.353 (127, 18)	0.195 (123, 18)	0.461 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Game Speed 10 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	-0.020 (1, 1)	-0.003 (1, 1)	0.765 (1, 1)	0.505 (1, 1)	0.075 (1, 1)
Minnesota	3.7 (47) / 0.44 (72)	-0.008 (9, 2)	-0.001 (10, 2)	0.576 (32, 4)	0.375 (29, 5)	0.239 (39, 7)
Nebraska	3.44 (19) / 0.39 (50)	-0.005 (23, 3)	-0.000 (23, 3)	0.647 (9, 3)	0.459 (4, 2)	0.216 (29, 4)
Ohio State	3.1 (4) / 0.48 (92)	-0.005 (26, 4)	-0.000 (25, 5)	0.647 (8, 2)	0.440 (7, 3)	0.200 (22, 3)
Wisconsin	3.77 (57) / 0.41 (61)	-0.005 (27, 5)	-0.000 (24, 4)	0.561 (38, 6)	0.432 (10, 4)	0.269 (57, 8)
Penn State	3.49 (23) / 0.48 (92)	-0.001 (43, 6)	0.000 (38, 6)	0.563 (36, 5)	0.257 (89, 12)	0.199 (20, 2)
Maryland	2.98 (2) / 0.37 (43)	0.002 (60, 8)	0.001 (59, 7)	0.493 (75, 11)	0.300 (65, 8)	0.315 (87, 13)
Purdue	3.52 (26) / 0.4 (53)	0.002 (59, 7)	0.001 (60, 8)	0.530 (54, 7)	0.357 (38, 6)	0.299 (76, 10)
Indiana	3.92 (80) / 0.47 (89)	0.004 (70, 9)	0.002 (68, 9)	0.471 (89, 13)	0.274 (82, 11)	0.318 (92, 14)
Michigan	3.36 (15) / 0.42 (64)	0.005 (77, 10)	0.002 (77, 10)	0.511 (67, 9)	0.214 (115, 15)	0.219 (32, 6)
Iowa	3.78 (59) / 0.43 (67)	0.006 (82, 11)	0.002 (83, 11)	0.522 (59, 8)	0.332 (51, 7)	0.305 (81, 11)
Rutgers	3.78 (59) / 0.47 (89)	0.007 (86, 12)	0.002 (86, 12)	0.476 (84, 12)	0.202 (119, 17)	0.217 (31, 5)
UCLA	3.93 (81) / 0.43 (67)	0.007 (90, 13)	0.002 (89, 13)	0.500 (72, 10)	0.291 (73, 9)	0.309 (83, 12)
Washington	3.53 (28) / 0.39 (50)	0.008 (96, 14)	0.002 (93, 14)	0.451 (97, 16)	0.229 (108, 14)	0.272 (59, 9)
Oregon	3.54 (29) / 0.5 (97)	0.012 (105, 15)	0.003 (105, 15)	0.457 (93, 14)	0.256 (90, 13)	0.334 (98, 15)
Illinois	3.58 (31) / 0.46 (84)	0.015 (113, 16)	0.004 (114, 16)	0.451 (96, 15)	0.280 (78, 10)	0.381 (114, 16)
Michigan State	3.76 (55) / 0.45 (79)	0.019 (127, 17)	0.005 (129, 17)	0.372 (125, 17)	0.185 (125, 18)	0.405 (122, 17)
USC	3.3 (11) / 0.46 (84)	0.023 (133, 18)	0.005 (133, 18)	0.368 (128, 18)	0.205 (118, 16)	0.468 (130, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Game Speed 40 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Oregon	3.54 (29) / 0.5 (97)	4.799 (14, 1)	4.722 (4, 1)	0.623 (10, 1)	0.424 (7, 1)	0.257 (33, 3)
Ohio State	3.1 (4) / 0.48 (92)	4.803 (16, 2)	4.773 (22, 2)	0.588 (20, 2)	0.336 (27, 2)	0.228 (19, 1)
Northwestern	3.86 (72) / 0.42 (64)	4.813 (22, 3)	4.791 (32, 4)	0.545 (39, 4)	0.319 (38, 4)	0.256 (31, 2)
Penn State	3.49 (23) / 0.48 (92)	4.823 (29, 4)	4.783 (26, 3)	0.571 (25, 3)	0.332 (30, 3)	0.262 (35, 4)
Maryland	2.98 (2) / 0.37 (43)	4.829 (35, 5)	4.798 (42, 5)	0.507 (58, 5)	0.291 (55, 6)	0.269 (39, 5)
Rutgers	3.78 (59) / 0.47 (89)	4.849 (61, 6)	4.831 (82, 7)	0.488 (70, 6)	0.301 (49, 5)	0.330 (80, 7)
Iowa	3.78 (59) / 0.43 (67)	4.858 (68, 7)	4.862 (108, 13)	0.487 (71, 7)	0.245 (86, 9)	0.303 (58, 6)
Wisconsin	3.77 (57) / 0.41 (61)	4.886 (93, 8)	4.831 (81, 6)	0.446 (92, 8)	0.243 (87, 10)	0.370 (102, 10)
Minnesota	3.7 (47) / 0.44 (72)	4.902 (105, 9)	4.855 (103, 11)	0.434 (98, 10)	0.185 (115, 14)	0.366 (99, 8)
Nebraska	3.44 (19) / 0.39 (50)	4.903 (107, 10)	4.835 (87, 8)	0.423 (107, 12)	0.191 (112, 13)	0.367 (100, 9)
Washington	3.53 (28) / 0.39 (50)	4.907 (111, 11)	4.839 (90, 9)	0.431 (100, 11)	0.282 (59, 7)	0.417 (122, 16)
Illinois	3.58 (31) / 0.46 (84)	4.909 (112, 12)	4.865 (111, 14)	0.437 (97, 9)	0.267 (68, 8)	0.404 (115, 13)
USC	3.3 (11) / 0.46 (84)	4.915 (114, 13)	4.875 (121, 15)	0.391 (118, 14)	0.192 (111, 12)	0.405 (118, 14)
Michigan State	3.76 (55) / 0.45 (79)	4.921 (116, 14)	4.861 (107, 12)	0.385 (121, 15)	0.119 (130, 18)	0.371 (103, 11)
Purdue	3.52 (26) / 0.4 (53)	4.930 (121, 15)	4.881 (123, 16)	0.349 (129, 17)	0.155 (122, 16)	0.411 (119, 15)
Michigan	3.36 (15) / 0.42 (64)	4.939 (124, 16)	4.843 (95, 10)	0.396 (116, 13)	0.178 (118, 15)	0.431 (127, 17)
Indiana	3.92 (80) / 0.47 (89)	4.944 (127, 17)	4.910 (132, 18)	0.371 (126, 16)	0.219 (101, 11)	0.443 (128, 18)
UCLA	3.93 (81) / 0.43 (67)	4.960 (131, 18)	4.897 (126, 17)	0.314 (134, 18)	0.119 (129, 17)	0.399 (114, 12)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Game Speed 40 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	-0.040 (6, 1)	-0.018 (6, 1)	0.629 (9, 1)	0.419 (10, 2)	0.194 (7, 1)
Oregon	3.54 (29) / 0.5 (97)	-0.040 (8, 2)	-0.018 (7, 2)	0.615 (11, 2)	0.459 (5, 1)	0.263 (41, 6)
Penn State	3.49 (23) / 0.48 (92)	-0.019 (21, 3)	-0.010 (21, 3)	0.575 (24, 3)	0.339 (29, 4)	0.237 (18, 2)
Rutgers	3.78 (59) / 0.47 (89)	-0.014 (30, 4)	-0.008 (35, 4)	0.505 (62, 8)	0.294 (60, 8)	0.256 (34, 3)
Illinois	3.58 (31) / 0.46 (84)	-0.013 (35, 5)	-0.007 (37, 5)	0.538 (40, 5)	0.405 (12, 3)	0.361 (102, 13)
Wisconsin	3.77 (57) / 0.41 (61)	-0.012 (41, 6)	-0.007 (38, 6)	0.537 (41, 6)	0.316 (44, 6)	0.262 (38, 5)
Ohio State	3.1 (4) / 0.48 (92)	-0.008 (50, 7)	-0.006 (51, 7)	0.541 (38, 4)	0.276 (74, 9)	0.282 (48, 8)
Maryland	2.98 (2) / 0.37 (43)	-0.006 (52, 8)	-0.005 (53, 8)	0.468 (87, 10)	0.203 (116, 14)	0.264 (42, 7)
Washington	3.53 (28) / 0.39 (50)	-0.003 (58, 9)	-0.005 (56, 9)	0.533 (42, 7)	0.304 (54, 7)	0.283 (50, 9)
Minnesota	3.7 (47) / 0.44 (72)	-0.000 (64, 10)	-0.004 (64, 10)	0.458 (95, 13)	0.264 (80, 10)	0.319 (85, 11)
Iowa	3.78 (59) / 0.43 (67)	0.001 (69, 11)	-0.002 (80, 12)	0.463 (92, 11)	0.257 (82, 11)	0.314 (78, 10)
Michigan	3.36 (15) / 0.42 (64)	0.005 (81, 12)	-0.002 (76, 11)	0.463 (93, 12)	0.194 (118, 16)	0.259 (36, 4)
Purdue	3.52 (26) / 0.4 (53)	0.012 (95, 13)	0.001 (95, 14)	0.472 (83, 9)	0.330 (35, 5)	0.394 (121, 16)
Nebraska	3.44 (19) / 0.39 (50)	0.014 (97, 14)	0.000 (89, 13)	0.455 (99, 14)	0.234 (100, 12)	0.338 (94, 12)
Michigan State	3.76 (55) / 0.45 (79)	0.022 (110, 15)	0.004 (108, 15)	0.398 (117, 15)	0.210 (111, 13)	0.379 (113, 14)
USC	3.3 (11) / 0.46 (84)	0.029 (119, 16)	0.007 (117, 16)	0.393 (119, 16)	0.162 (129, 17)	0.393 (120, 15)
UCLA	3.93 (81) / 0.43 (67)	0.034 (122, 17)	0.008 (122, 17)	0.356 (127, 17)	0.198 (117, 15)	0.444 (129, 17)
Indiana	3.92 (80) / 0.47 (89)	0.055 (134, 18)	0.016 (134, 18)	0.321 (133, 18)	0.158 (130, 18)	0.474 (133, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Game Speed 40 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	-0.085 (2, 1)	-0.005 (2, 1)	0.765 (1, 1)	0.561 (1, 1)	0.095 (2, 1)
Rutgers	3.78 (59) / 0.47 (89)	-0.036 (14, 2)	0.003 (14, 2)	0.613 (17, 4)	0.413 (11, 2)	0.207 (20, 6)
Penn State	3.49 (23) / 0.48 (92)	-0.026 (17, 3)	0.003 (16, 3)	0.639 (11, 2)	0.329 (47, 8)	0.173 (9, 2)
Maryland	2.98 (2) / 0.37 (43)	-0.023 (19, 4)	0.005 (18, 4)	0.526 (51, 9)	0.277 (70, 10)	0.196 (15, 5)
Nebraska	3.44 (19) / 0.39 (50)	-0.012 (23, 5)	0.007 (23, 5)	0.556 (34, 7)	0.333 (42, 7)	0.247 (33, 7)
Wisconsin	3.77 (57) / 0.41 (61)	-0.009 (26, 6)	0.008 (26, 6)	0.546 (38, 8)	0.340 (38, 6)	0.250 (36, 8)
Ohio State	3.1 (4) / 0.48 (92)	-0.004 (34, 7)	0.008 (33, 7)	0.625 (14, 3)	0.253 (82, 12)	0.175 (11, 3)
Washington	3.53 (28) / 0.39 (50)	0.003 (42, 8)	0.010 (42, 8)	0.576 (23, 6)	0.242 (90, 14)	0.181 (12, 4)
Iowa	3.78 (59) / 0.43 (67)	0.011 (53, 9)	0.011 (55, 9)	0.602 (19, 5)	0.391 (14, 3)	0.274 (47, 9)
Purdue	3.52 (26) / 0.4 (53)	0.019 (64, 10)	0.012 (65, 10)	0.522 (55, 11)	0.376 (23, 4)	0.360 (108, 14)
Oregon	3.54 (29) / 0.5 (97)	0.023 (70, 11)	0.013 (70, 11)	0.525 (52, 10)	0.316 (54, 9)	0.312 (73, 12)
Michigan	3.36 (15) / 0.42 (64)	0.028 (80, 12)	0.014 (80, 12)	0.459 (92, 14)	0.223 (103, 16)	0.285 (59, 10)
Minnesota	3.7 (47) / 0.44 (72)	0.030 (82, 13)	0.014 (83, 13)	0.433 (103, 15)	0.247 (87, 13)	0.365 (111, 15)
Michigan State	3.76 (55) / 0.45 (79)	0.031 (84, 14)	0.015 (94, 14)	0.479 (73, 12)	0.260 (78, 11)	0.312 (72, 11)
Illinois	3.58 (31) / 0.46 (84)	0.045 (103, 16)	0.017 (107, 16)	0.463 (89, 13)	0.342 (37, 5)	0.434 (131, 18)
USC	3.3 (11) / 0.46 (84)	0.045 (102, 15)	0.017 (106, 15)	0.423 (111, 16)	0.201 (115, 17)	0.342 (99, 13)
UCLA	3.93 (81) / 0.43 (67)	0.068 (121, 17)	0.020 (122, 17)	0.400 (116, 17)	0.229 (100, 15)	0.394 (119, 16)
Indiana	3.92 (80) / 0.47 (89)	0.081 (129, 18)	0.022 (129, 18)	0.386 (120, 18)	0.190 (117, 18)	0.398 (121, 17)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Game Speed Flying 20 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Maryland	2.98 (2) / 0.37 (43)	1.957 (17, 1)	1.944 (22, 2)	0.535 (41, 4)	0.314 (35, 3)	0.246 (25, 2)
Ohio State	3.1 (4) / 0.48 (92)	1.962 (25, 2)	1.946 (23, 3)	0.590 (16, 2)	0.301 (48, 5)	0.231 (17, 1)
Oregon	3.54 (29) / 0.5 (97)	1.964 (28, 3)	1.923 (7, 1)	0.606 (11, 1)	0.403 (6, 1)	0.258 (31, 3)
Penn State	3.49 (23) / 0.48 (92)	1.967 (32, 4)	1.949 (28, 4)	0.555 (30, 3)	0.340 (25, 2)	0.274 (41, 4)
Rutgers	3.78 (59) / 0.47 (89)	1.971 (37, 5)	1.964 (55, 5)	0.486 (70, 6)	0.291 (56, 7)	0.292 (53, 5)
Northwestern	3.86 (72) / 0.42 (64)	1.973 (40, 6)	1.964 (59, 6)	0.502 (58, 5)	0.301 (49, 6)	0.299 (60, 6)
Iowa	3.78 (59) / 0.43 (67)	1.996 (79, 7)	1.996 (113, 14)	0.454 (89, 8)	0.228 (96, 10)	0.339 (86, 7)
Wisconsin	3.77 (57) / 0.41 (61)	1.999 (91, 8)	1.969 (67, 7)	0.443 (97, 9)	0.275 (63, 9)	0.382 (107, 12)
Illinois	3.58 (31) / 0.46 (84)	2.008 (103, 9)	1.988 (102, 11)	0.471 (84, 7)	0.303 (46, 4)	0.391 (111, 13)
USC	3.3 (11) / 0.46 (84)	2.010 (105, 10)	1.990 (108, 13)	0.405 (115, 14)	0.205 (108, 13)	0.376 (106, 11)
Washington	3.53 (28) / 0.39 (50)	2.012 (107, 11)	1.979 (90, 8)	0.441 (100, 10)	0.280 (61, 8)	0.400 (118, 14)
Michigan State	3.76 (55) / 0.45 (79)	2.015 (108, 12)	1.988 (104, 12)	0.431 (103, 12)	0.112 (132, 18)	0.364 (98, 9)
Nebraska	3.44 (19) / 0.39 (50)	2.017 (109, 13)	1.981 (95, 10)	0.391 (119, 15)	0.144 (128, 17)	0.351 (92, 8)
Minnesota	3.7 (47) / 0.44 (72)	2.020 (115, 14)	1.997 (115, 15)	0.432 (102, 11)	0.163 (124, 15)	0.366 (100, 10)
Michigan	3.36 (15) / 0.42 (64)	2.027 (119, 15)	1.979 (91, 9)	0.417 (109, 13)	0.226 (98, 11)	0.425 (126, 17)
Purdue	3.52 (26) / 0.4 (53)	2.028 (120, 16)	2.004 (122, 16)	0.352 (129, 16)	0.169 (122, 14)	0.415 (122, 15)
Indiana	3.92 (80) / 0.47 (89)	2.040 (130, 17)	2.025 (132, 18)	0.349 (130, 17)	0.224 (101, 12)	0.445 (131, 18)
UCLA	3.93 (81) / 0.43 (67)	2.045 (131, 18)	2.009 (125, 17)	0.342 (132, 18)	0.145 (127, 16)	0.417 (124, 16)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Game Speed Flying 20 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Oregon	3.54 (29) / 0.5 (97)	-0.023 (8, 1)	-0.008 (7, 1)	0.587 (16, 1)	0.435 (6, 1)	0.275 (55, 7)
Maryland	2.98 (2) / 0.37 (43)	-0.014 (23, 4)	-0.005 (24, 4)	0.500 (71, 9)	0.277 (73, 10)	0.229 (21, 1)
Northwestern	3.86 (72) / 0.42 (64)	-0.014 (22, 3)	-0.005 (21, 2)	0.582 (21, 2)	0.367 (22, 3)	0.234 (24, 2)
Rutgers	3.78 (59) / 0.47 (89)	-0.014 (21, 2)	-0.005 (22, 3)	0.545 (42, 5)	0.343 (32, 4)	0.253 (37, 5)
Penn State	3.49 (23) / 0.48 (92)	-0.012 (28, 5)	-0.005 (26, 5)	0.550 (40, 4)	0.331 (37, 5)	0.252 (36, 4)
Illinois	3.58 (31) / 0.46 (84)	-0.011 (33, 6)	-0.004 (32, 6)	0.554 (38, 3)	0.378 (16, 2)	0.301 (70, 10)
Wisconsin	3.77 (57) / 0.41 (61)	-0.008 (40, 7)	-0.003 (39, 7)	0.524 (52, 6)	0.292 (63, 8)	0.250 (35, 3)
Ohio State	3.1 (4) / 0.48 (92)	-0.003 (58, 8)	-0.002 (56, 8)	0.488 (77, 11)	0.291 (64, 9)	0.299 (68, 9)
Iowa	3.78 (59) / 0.43 (67)	0.001 (69, 9)	-0.000 (75, 11)	0.503 (65, 8)	0.274 (75, 11)	0.282 (59, 8)
Washington	3.53 (28) / 0.39 (50)	0.001 (70, 10)	-0.001 (69, 9)	0.513 (59, 7)	0.327 (42, 6)	0.312 (81, 11)
Michigan	3.36 (15) / 0.42 (64)	0.003 (75, 11)	-0.001 (72, 10)	0.495 (74, 10)	0.208 (117, 15)	0.261 (43, 6)
Minnesota	3.7 (47) / 0.44 (72)	0.008 (92, 12)	0.001 (93, 12)	0.439 (104, 13)	0.244 (101, 12)	0.337 (94, 14)
Michigan State	3.76 (55) / 0.45 (79)	0.010 (101, 13)	0.002 (96, 13)	0.437 (108, 14)	0.216 (113, 13)	0.320 (88, 12)
Purdue	3.52 (26) / 0.4 (53)	0.011 (102, 14)	0.002 (103, 14)	0.454 (99, 12)	0.304 (50, 7)	0.394 (124, 16)
USC	3.3 (11) / 0.46 (84)	0.013 (109, 15)	0.002 (107, 16)	0.428 (111, 15)	0.212 (115, 14)	0.357 (108, 15)
Nebraska	3.44 (19) / 0.39 (50)	0.015 (112, 16)	0.002 (106, 15)	0.406 (122, 16)	0.160 (129, 18)	0.322 (89, 13)
UCLA	3.93 (81) / 0.43 (67)	0.021 (122, 17)	0.005 (122, 17)	0.303 (134, 18)	0.207 (118, 16)	0.446 (131, 17)
Indiana	3.92 (80) / 0.47 (89)	0.038 (132, 18)	0.010 (133, 18)	0.345 (128, 17)	0.191 (124, 17)	0.470 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Game Speed Flying 20 | 2024 - 2025 | WR / RB / CB / S / LB / TE
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	-0.044 (3, 1)	0.001 (3, 1)	0.691 (3, 1)	0.483 (3, 1)	0.148 (6, 1)
Rutgers	3.78 (59) / 0.47 (89)	-0.035 (5, 2)	0.002 (5, 2)	0.656 (7, 2)	0.464 (5, 2)	0.186 (18, 4)
Penn State	3.49 (23) / 0.48 (92)	-0.020 (17, 3)	0.003 (14, 3)	0.629 (14, 3)	0.336 (41, 6)	0.160 (9, 2)
Maryland	2.98 (2) / 0.37 (43)	-0.018 (19, 4)	0.004 (19, 4)	0.528 (49, 8)	0.316 (49, 8)	0.229 (31, 6)
Nebraska	3.44 (19) / 0.39 (50)	-0.006 (36, 6)	0.005 (36, 6)	0.519 (53, 9)	0.286 (69, 11)	0.254 (45, 9)
Wisconsin	3.77 (57) / 0.41 (61)	-0.006 (35, 5)	0.005 (35, 5)	0.547 (41, 7)	0.309 (53, 9)	0.229 (32, 7)
Washington	3.53 (28) / 0.39 (50)	-0.003 (38, 7)	0.006 (38, 7)	0.576 (23, 4)	0.296 (60, 10)	0.179 (15, 3)
Ohio State	3.1 (4) / 0.48 (92)	0.001 (51, 8)	0.006 (49, 8)	0.558 (35, 6)	0.282 (73, 12)	0.221 (28, 5)
Iowa	3.78 (59) / 0.43 (67)	0.002 (52, 9)	0.006 (52, 9)	0.566 (28, 5)	0.374 (24, 3)	0.274 (54, 11)
Michigan State	3.76 (55) / 0.45 (79)	0.007 (60, 11)	0.007 (63, 11)	0.501 (66, 11)	0.260 (84, 13)	0.257 (46, 10)
Oregon	3.54 (29) / 0.5 (97)	0.007 (59, 10)	0.007 (58, 10)	0.518 (55, 10)	0.320 (47, 7)	0.308 (75, 13)
USC	3.3 (11) / 0.46 (84)	0.012 (69, 12)	0.008 (74, 12)	0.460 (89, 14)	0.192 (112, 17)	0.242 (37, 8)
Purdue	3.52 (26) / 0.4 (53)	0.013 (74, 13)	0.008 (75, 13)	0.496 (69, 12)	0.366 (28, 4)	0.373 (110, 14)
Michigan	3.36 (15) / 0.42 (64)	0.016 (82, 14)	0.008 (82, 14)	0.456 (93, 15)	0.241 (94, 14)	0.304 (72, 12)
Illinois	3.58 (31) / 0.46 (84)	0.018 (84, 15)	0.009 (90, 15)	0.485 (78, 13)	0.361 (30, 5)	0.412 (128, 17)
Minnesota	3.7 (47) / 0.44 (72)	0.030 (103, 16)	0.010 (103, 16)	0.429 (108, 16)	0.224 (101, 16)	0.390 (118, 15)
UCLA	3.93 (81) / 0.43 (67)	0.044 (126, 17)	0.012 (125, 17)	0.416 (112, 17)	0.237 (95, 15)	0.391 (120, 16)
Indiana	3.92 (80) / 0.47 (89)	0.056 (131, 18)	0.013 (131, 18)	0.369 (125, 18)	0.189 (114, 18)	0.432 (131, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Change Of Direction | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.537 (9, 1)	0.568 (7, 1)	0.704 (16, 3)	0.479 (12, 2)	0.108 (16, 3)
Nebraska	3.44 (19) / 0.39 (50)	0.507 (13, 2)	0.519 (17, 2)	0.902 (3, 1)	0.467 (15, 3)	0.002 (3, 1)
UCLA	3.93 (81) / 0.43 (67)	0.499 (14, 3)	0.501 (35, 3)	0.739 (9, 2)	0.559 (8, 1)	0.173 (30, 5)
Illinois	3.58 (31) / 0.46 (84)	0.444 (43, 4)	0.477 (54, 5)	0.497 (65, 7)	0.364 (44, 4)	0.275 (62, 7)
Wisconsin	3.77 (57) / 0.41 (61)	0.440 (48, 5)	0.489 (47, 4)	0.620 (26, 4)	0.272 (78, 10)	0.099 (14, 2)
Michigan State	3.76 (55) / 0.45 (79)	0.416 (59, 6)	0.464 (68, 9)	0.541 (53, 6)	0.355 (46, 5)	0.296 (67, 8)
Purdue	3.52 (26) / 0.4 (53)	0.408 (66, 7)	0.477 (55, 6)	0.582 (37, 5)	0.229 (98, 11)	0.144 (22, 4)
Oregon	3.54 (29) / 0.5 (97)	0.392 (80, 8)	0.467 (59, 7)	0.484 (71, 8)	0.352 (47, 6)	0.387 (95, 10)
Penn State	3.49 (23) / 0.48 (92)	0.390 (82, 9)	0.450 (86, 10)	0.373 (112, 15)	0.228 (99, 12)	0.467 (120, 16)
Michigan	3.36 (15) / 0.42 (64)	0.384 (89, 10)	0.466 (63, 8)	0.445 (86, 11)	0.094 (125, 16)	0.211 (40, 6)
Indiana	3.92 (80) / 0.47 (89)	0.382 (92, 11)	0.437 (101, 11)	0.453 (83, 10)	0.282 (74, 8)	0.388 (97, 11)
Ohio State	3.1 (4) / 0.48 (92)	0.347 (107, 12)	0.415 (113, 12)	0.462 (82, 9)	0.315 (61, 7)	0.421 (105, 13)
Maryland	2.98 (2) / 0.37 (43)	0.334 (114, 13)	0.413 (114, 13)	0.204 (130, 17)	0.095 (124, 15)	0.361 (85, 9)
Rutgers	3.78 (59) / 0.47 (89)	0.331 (116, 14)	0.396 (122, 15)	0.384 (108, 13)	0.197 (112, 14)	0.422 (106, 14)
Minnesota	3.7 (47) / 0.44 (72)	0.314 (122, 15)	0.395 (123, 16)	0.200 (132, 18)	0.015 (132, 18)	0.389 (98, 12)
Iowa	3.78 (59) / 0.43 (67)	0.299 (125, 16)	0.405 (121, 14)	0.384 (109, 14)	0.278 (76, 9)	0.504 (124, 17)
USC	3.3 (11) / 0.46 (84)	0.297 (126, 17)	0.368 (131, 17)	0.385 (106, 12)	0.218 (104, 13)	0.445 (117, 15)
Washington	3.53 (28) / 0.39 (50)	0.266 (131, 18)	0.361 (133, 18)	0.224 (129, 16)	0.060 (128, 17)	0.557 (128, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Change Of Direction | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.122 (2, 1)	0.034 (2, 1)	0.848 (5, 1)	0.688 (2, 1)	0.030 (6, 1)
Nebraska	3.44 (19) / 0.39 (50)	0.076 (12, 2)	0.023 (12, 2)	0.784 (9, 2)	0.544 (11, 2)	0.054 (8, 2)
Wisconsin	3.77 (57) / 0.41 (61)	0.034 (36, 3)	0.013 (35, 3)	0.548 (46, 5)	0.304 (66, 10)	0.110 (16, 4)
Michigan	3.36 (15) / 0.42 (64)	0.026 (42, 4)	0.011 (42, 4)	0.626 (23, 3)	0.335 (56, 8)	0.109 (15, 3)
Purdue	3.52 (26) / 0.4 (53)	0.026 (43, 5)	0.011 (43, 5)	0.604 (28, 4)	0.379 (35, 4)	0.210 (42, 5)
UCLA	3.93 (81) / 0.43 (67)	0.019 (50, 6)	0.010 (50, 6)	0.535 (53, 7)	0.308 (65, 9)	0.228 (46, 6)
Penn State	3.49 (23) / 0.48 (92)	0.015 (55, 7)	0.009 (55, 7)	0.374 (110, 13)	0.280 (73, 11)	0.412 (110, 13)
Illinois	3.58 (31) / 0.46 (84)	0.005 (68, 8)	0.006 (68, 8)	0.520 (60, 9)	0.374 (37, 5)	0.338 (86, 9)
Michigan State	3.76 (55) / 0.45 (79)	-0.003 (75, 9)	0.005 (75, 9)	0.516 (64, 10)	0.351 (46, 6)	0.339 (87, 10)
Oregon	3.54 (29) / 0.5 (97)	-0.006 (80, 10)	0.004 (80, 10)	0.521 (59, 8)	0.407 (30, 3)	0.317 (77, 7)
Iowa	3.78 (59) / 0.43 (67)	-0.023 (99, 11)	-0.000 (99, 11)	0.543 (52, 6)	0.347 (49, 7)	0.383 (103, 11)
Indiana	3.92 (80) / 0.47 (89)	-0.025 (100, 12)	-0.000 (100, 12)	0.413 (99, 12)	0.186 (107, 14)	0.335 (85, 8)
Ohio State	3.1 (4) / 0.48 (92)	-0.025 (101, 13)	-0.001 (101, 13)	0.426 (96, 11)	0.262 (82, 12)	0.399 (107, 12)
Maryland	2.98 (2) / 0.37 (43)	-0.054 (120, 14)	-0.007 (120, 14)	0.304 (119, 16)	0.125 (122, 16)	0.457 (117, 14)
Rutgers	3.78 (59) / 0.47 (89)	-0.061 (121, 15)	-0.009 (121, 15)	0.356 (114, 14)	0.237 (92, 13)	0.511 (126, 16)
Minnesota	3.7 (47) / 0.44 (72)	-0.063 (122, 16)	-0.010 (122, 16)	0.313 (118, 15)	0.149 (116, 15)	0.496 (122, 15)
Washington	3.53 (28) / 0.39 (50)	-0.082 (129, 17)	-0.014 (129, 17)	0.149 (132, 18)	0.031 (129, 18)	0.527 (130, 17)
USC	3.3 (11) / 0.46 (84)	-0.096 (130, 18)	-0.017 (130, 18)	0.231 (129, 17)	0.096 (125, 17)	0.579 (131, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Change Of Direction | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.142 (5, 1)	0.011 (5, 1)	0.741 (18, 2)	0.560 (9, 1)	0.134 (30, 4)
Purdue	3.52 (26) / 0.4 (53)	0.090 (15, 2)	0.007 (15, 2)	0.560 (48, 7)	0.500 (16, 3)	0.008 (14, 1)
Michigan	3.36 (15) / 0.42 (64)	0.082 (17, 3)	0.007 (17, 3)	0.657 (31, 5)	0.474 (20, 4)	0.185 (37, 5)
Nebraska	3.44 (19) / 0.39 (50)	0.079 (19, 4)	0.006 (19, 4)	0.697 (22, 3)	0.518 (12, 2)	0.065 (21, 2)
Oregon	3.54 (29) / 0.5 (97)	0.032 (41, 5)	0.003 (40, 5)	0.569 (45, 6)	0.380 (41, 6)	0.249 (52, 8)
Minnesota	3.7 (47) / 0.44 (72)	0.027 (45, 6)	0.002 (45, 6)	0.674 (26, 4)	0.327 (57, 8)	0.222 (46, 7)
Iowa	3.78 (59) / 0.43 (67)	0.023 (49, 7)	0.002 (48, 7)	0.502 (65, 10)	0.382 (39, 5)	0.334 (76, 11)
Ohio State	3.1 (4) / 0.48 (92)	0.020 (51, 8)	0.002 (50, 8)	0.753 (15, 1)	0.228 (83, 12)	0.100 (24, 3)
Wisconsin	3.77 (57) / 0.41 (61)	0.013 (61, 9)	0.001 (63, 9)	0.524 (59, 9)	0.351 (51, 7)	0.304 (67, 9)
Penn State	3.49 (23) / 0.48 (92)	-0.021 (78, 10)	-0.001 (77, 10)	0.413 (92, 12)	0.204 (91, 15)	0.213 (45, 6)
UCLA	3.93 (81) / 0.43 (67)	-0.041 (91, 11)	-0.003 (90, 11)	0.331 (111, 16)	0.221 (85, 13)	0.397 (96, 13)
Michigan State	3.76 (55) / 0.45 (79)	-0.068 (101, 12)	-0.005 (101, 12)	0.418 (90, 11)	0.289 (66, 9)	0.450 (112, 16)
Illinois	3.58 (31) / 0.46 (84)	-0.073 (104, 13)	-0.005 (104, 13)	0.392 (100, 13)	0.237 (80, 11)	0.437 (107, 14)
Indiana	3.92 (80) / 0.47 (89)	-0.083 (105, 14)	-0.006 (105, 14)	0.362 (103, 14)	0.185 (99, 16)	0.441 (109, 15)
USC	3.3 (11) / 0.46 (84)	-0.088 (109, 15)	-0.007 (109, 15)	0.525 (58, 8)	0.250 (76, 10)	0.385 (92, 12)
Maryland	2.98 (2) / 0.37 (43)	-0.097 (115, 16)	-0.007 (115, 16)	0.002 (132, 18)	0.000 (132, 18)	0.329 (73, 10)
Washington	3.53 (28) / 0.39 (50)	-0.100 (116, 17)	-0.007 (116, 17)	0.262 (123, 17)	0.098 (113, 17)	0.478 (118, 17)
Rutgers	3.78 (59) / 0.47 (89)	-0.115 (125, 18)	-0.009 (125, 18)	0.340 (108, 15)	0.220 (86, 14)	0.521 (124, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Deceleration | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.489 (6, 1)	0.475 (8, 1)	0.800 (4, 1)	0.640 (2, 1)	0.148 (18, 2)
Wisconsin	3.77 (57) / 0.41 (61)	0.448 (18, 2)	0.457 (17, 2)	0.626 (29, 3)	0.260 (76, 8)	0.071 (7, 1)
Nebraska	3.44 (19) / 0.39 (50)	0.434 (24, 3)	0.448 (25, 3)	0.640 (23, 2)	0.433 (22, 2)	0.221 (33, 4)
UCLA	3.93 (81) / 0.43 (67)	0.415 (38, 4)	0.436 (39, 4)	0.536 (58, 5)	0.314 (58, 5)	0.174 (23, 3)
Michigan State	3.76 (55) / 0.45 (79)	0.385 (52, 5)	0.425 (50, 5)	0.600 (36, 4)	0.368 (45, 3)	0.222 (36, 5)
Indiana	3.92 (80) / 0.47 (89)	0.355 (73, 6)	0.408 (74, 7)	0.467 (77, 8)	0.281 (66, 7)	0.369 (97, 9)
Rutgers	3.78 (59) / 0.47 (89)	0.355 (74, 7)	0.401 (83, 9)	0.495 (69, 6)	0.312 (59, 6)	0.342 (90, 8)
Iowa	3.78 (59) / 0.43 (67)	0.349 (78, 8)	0.416 (62, 6)	0.376 (102, 10)	0.325 (54, 4)	0.460 (114, 11)
Penn State	3.49 (23) / 0.48 (92)	0.348 (79, 9)	0.401 (81, 8)	0.471 (76, 7)	0.160 (119, 14)	0.287 (66, 7)
Purdue	3.52 (26) / 0.4 (53)	0.344 (84, 10)	0.398 (87, 10)	0.356 (108, 12)	0.136 (124, 16)	0.253 (50, 6)
USC	3.3 (11) / 0.46 (84)	0.303 (110, 11)	0.379 (111, 11)	0.393 (97, 9)	0.225 (92, 10)	0.426 (110, 10)
Ohio State	3.1 (4) / 0.48 (92)	0.300 (112, 12)	0.379 (112, 12)	0.335 (116, 13)	0.241 (82, 9)	0.488 (121, 16)
Michigan	3.36 (15) / 0.42 (64)	0.299 (113, 13)	0.377 (115, 13)	0.294 (128, 16)	0.193 (108, 13)	0.461 (115, 12)
Illinois	3.58 (31) / 0.46 (84)	0.285 (118, 14)	0.367 (122, 15)	0.365 (105, 11)	0.215 (95, 11)	0.463 (116, 13)
Oregon	3.54 (29) / 0.5 (97)	0.284 (120, 15)	0.373 (118, 14)	0.273 (130, 17)	0.072 (130, 17)	0.464 (117, 14)
Washington	3.53 (28) / 0.39 (50)	0.240 (127, 16)	0.344 (127, 16)	0.300 (127, 15)	0.141 (122, 15)	0.508 (124, 17)
Maryland	2.98 (2) / 0.37 (43)	0.217 (131, 17)	0.336 (131, 17)	0.258 (131, 18)	0.050 (132, 18)	0.477 (120, 15)
Minnesota	3.7 (47) / 0.44 (72)	0.205 (132, 18)	0.326 (133, 18)	0.309 (126, 14)	0.200 (106, 12)	0.567 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Performance Summary: Deceleration | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Northwestern	3.86 (72) / 0.42 (64)	0.081 (5, 1)	0.016 (5, 1)	0.776 (3, 1)	0.548 (3, 1)	0.149 (13, 2)
Wisconsin	3.77 (57) / 0.41 (61)	0.074 (9, 2)	0.016 (9, 2)	0.748 (4, 2)	0.526 (5, 2)	0.051 (4, 1)
Nebraska	3.44 (19) / 0.39 (50)	0.053 (23, 3)	0.014 (22, 3)	0.587 (36, 4)	0.413 (28, 4)	0.253 (42, 4)
UCLA	3.93 (81) / 0.43 (67)	0.052 (24, 4)	0.014 (24, 4)	0.592 (34, 3)	0.437 (21, 3)	0.275 (50, 5)
Michigan State	3.76 (55) / 0.45 (79)	0.006 (65, 5)	0.010 (65, 5)	0.578 (39, 5)	0.292 (61, 5)	0.195 (22, 3)
Penn State	3.49 (23) / 0.48 (92)	0.005 (67, 6)	0.010 (67, 6)	0.505 (62, 6)	0.229 (91, 9)	0.284 (54, 6)
Iowa	3.78 (59) / 0.43 (67)	-0.006 (90, 7)	0.009 (90, 7)	0.403 (109, 12)	0.215 (100, 11)	0.344 (85, 8)
Indiana	3.92 (80) / 0.47 (89)	-0.007 (92, 8)	0.009 (92, 8)	0.416 (102, 10)	0.248 (85, 8)	0.391 (103, 10)
Ohio State	3.1 (4) / 0.48 (92)	-0.013 (101, 9)	0.009 (101, 9)	0.424 (95, 8)	0.282 (66, 6)	0.398 (106, 11)
Michigan	3.36 (15) / 0.42 (64)	-0.018 (106, 10)	0.008 (106, 10)	0.465 (73, 7)	0.205 (106, 12)	0.334 (80, 7)
Purdue	3.52 (26) / 0.4 (53)	-0.026 (109, 11)	0.008 (109, 11)	0.424 (96, 9)	0.165 (122, 15)	0.382 (100, 9)
USC	3.3 (11) / 0.46 (84)	-0.028 (110, 12)	0.008 (110, 12)	0.376 (116, 14)	0.218 (98, 10)	0.420 (113, 13)
Rutgers	3.78 (59) / 0.47 (89)	-0.029 (111, 13)	0.007 (111, 13)	0.391 (113, 13)	0.196 (109, 13)	0.411 (109, 12)
Illinois	3.58 (31) / 0.46 (84)	-0.039 (118, 14)	0.007 (118, 14)	0.408 (106, 11)	0.273 (70, 7)	0.451 (120, 15)
Oregon	3.54 (29) / 0.5 (97)	-0.047 (124, 15)	0.006 (124, 15)	0.319 (125, 16)	0.084 (130, 17)	0.431 (118, 14)
Washington	3.53 (28) / 0.39 (50)	-0.068 (129, 16)	0.004 (129, 16)	0.256 (129, 17)	0.105 (127, 16)	0.524 (130, 17)
Minnesota	3.7 (47) / 0.44 (72)	-0.086 (132, 17)	0.003 (132, 17)	0.360 (120, 15)	0.182 (114, 14)	0.472 (125, 16)
Maryland	2.98 (2) / 0.37 (43)	-0.087 (133, 18)	0.003 (133, 18)	0.207 (133, 18)	0.074 (131, 18)	0.550 (132, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten OE Development Summary: Deceleration | 2024 - 2025 | WR / CB / S
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Wisconsin	3.77 (57) / 0.41 (61)	0.083 (18, 1)	-0.005 (18, 1)	0.750 (11, 1)	0.418 (29, 2)	0.044 (14, 1)
UCLA	3.93 (81) / 0.43 (67)	0.063 (27, 2)	-0.006 (27, 2)	0.600 (33, 2)	0.493 (16, 1)	0.302 (60, 5)
Ohio State	3.1 (4) / 0.48 (92)	0.020 (49, 3)	-0.008 (49, 3)	0.547 (50, 3)	0.381 (40, 3)	0.298 (58, 4)
Northwestern	3.86 (72) / 0.42 (64)	-0.016 (70, 4)	-0.009 (70, 4)	0.531 (57, 4)	0.348 (53, 5)	0.291 (55, 3)
Michigan	3.36 (15) / 0.42 (64)	-0.019 (72, 5)	-0.009 (71, 5)	0.414 (97, 11)	0.170 (112, 13)	0.219 (38, 2)
Washington	3.53 (28) / 0.39 (50)	-0.035 (76, 6)	-0.009 (76, 6)	0.483 (75, 7)	0.324 (58, 6)	0.366 (84, 7)
Penn State	3.49 (23) / 0.48 (92)	-0.051 (90, 7)	-0.010 (90, 7)	0.421 (92, 10)	0.197 (105, 11)	0.343 (75, 6)
Nebraska	3.44 (19) / 0.39 (50)	-0.052 (93, 8)	-0.010 (93, 8)	0.461 (79, 8)	0.271 (81, 8)	0.366 (85, 8)
Maryland	2.98 (2) / 0.37 (43)	-0.054 (98, 9)	-0.010 (99, 9)	0.516 (63, 5)	0.374 (43, 4)	0.386 (92, 10)
USC	3.3 (11) / 0.46 (84)	-0.057 (103, 10)	-0.010 (103, 10)	0.396 (104, 13)	0.235 (95, 10)	0.409 (107, 13)
Oregon	3.54 (29) / 0.5 (97)	-0.068 (106, 11)	-0.011 (106, 11)	0.399 (102, 12)	0.171 (110, 12)	0.368 (87, 9)
Illinois	3.58 (31) / 0.46 (84)	-0.070 (108, 12)	-0.011 (108, 12)	0.491 (71, 6)	0.305 (71, 7)	0.399 (102, 12)
Minnesota	3.7 (47) / 0.44 (72)	-0.073 (109, 13)	-0.011 (109, 13)	0.445 (85, 9)	0.262 (87, 9)	0.410 (108, 14)
Michigan State	3.76 (55) / 0.45 (79)	-0.088 (117, 14)	-0.011 (117, 14)	0.383 (107, 14)	0.153 (118, 15)	0.390 (96, 11)
Rutgers	3.78 (59) / 0.47 (89)	-0.102 (120, 15)	-0.012 (120, 15)	0.230 (125, 17)	0.013 (130, 17)	0.523 (121, 16)
Indiana	3.92 (80) / 0.47 (89)	-0.125 (123, 16)	-0.013 (123, 16)	0.287 (117, 15)	0.155 (117, 14)	0.538 (122, 17)
Iowa	3.78 (59) / 0.43 (67)	-0.131 (125, 17)	-0.013 (125, 17)	0.281 (119, 16)	0.098 (124, 16)	0.494 (120, 15)
Purdue	3.52 (26) / 0.4 (53)	-0.224 (132, 18)	-0.016 (132, 18)	0.029 (133, 18)	0.001 (133, 18)	0.770 (133, 18)
Note: Value(Nat. Rank, Conf. Rank)

Big Ten Raw Performance Summary: Dl Burst | 2024 - 2025 | DT / DE / LB
Team	Age(%) / YOE(%)	Average	Effect	Prob +	Prob SWC	Prob SWD
Illinois	3.58 (31) / 0.46 (84)	0.096 (10, 1)	0.095 (22, 3)	0.656 (9, 1)	0.507 (30, 2)	0.191 (24, 2)
Michigan	3.36 (15) / 0.42 (64)	0.094 (16, 2)	0.096 (16, 2)	0.567 (40, 5)	0.496 (33, 4)	0.204 (32, 4)
Penn State	3.49 (23) / 0.48 (92)	0.092 (30, 4)	0.102 (2, 1)	0.464 (98, 12)	0.369 (101, 13)	0.103 (7, 1)
Washington	3.53 (28) / 0.39 (50)	0.092 (29, 3)	0.092 (59, 9)	0.612 (20, 2)	0.504 (31, 3)	0.248 (53, 5)
Nebraska	3.44 (19) / 0.39 (50)	0.091 (38, 5)	0.095 (25, 4)	0.555 (46, 6)	0.487 (36, 5)	0.283 (72, 10)
Oregon	3.54 (29) / 0.5 (97)	0.089 (49, 6)	0.090 (83, 14)	0.574 (36, 3)	0.477 (40, 6)	0.273 (65, 8)
Maryland	2.98 (2) / 0.37 (43)	0.088 (56, 7)	0.091 (72, 12)	0.568 (39, 4)	0.525 (23, 1)	0.249 (54, 6)
Wisconsin	3.77 (57) / 0.41 (61)	0.087 (59, 8)	0.093 (53, 7)	0.485 (88, 10)	0.385 (91, 11)	0.277 (67, 9)
Indiana	3.92 (80) / 0.47 (89)	0.085 (80, 9)	0.092 (60, 10)	0.512 (71, 7)	0.337 (110, 14)	0.193 (27, 3)
Iowa	3.78 (59) / 0.43 (67)	0.084 (85, 11)	0.094 (36, 6)	0.424 (117, 15)	0.395 (84, 8)	0.351 (101, 14)
Minnesota	3.7 (47) / 0.44 (72)	0.084 (90, 12)	0.092 (62, 11)	0.461 (100, 13)	0.405 (80, 7)	0.308 (86, 12)
USC	3.3 (11) / 0.46 (84)	0.084 (82, 10)	0.095 (29, 5)	0.431 (114, 14)	0.335 (112, 15)	0.256 (59, 7)
Michigan State	3.76 (55) / 0.45 (79)	0.083 (92, 13)	0.090 (73, 13)	0.476 (91, 11)	0.383 (93, 12)	0.314 (89, 13)
Ohio State	3.1 (4) / 0.48 (92)	0.083 (96, 14)	0.092 (57, 8)	0.401 (121, 17)	0.318 (117, 16)	0.283 (75, 11)
Rutgers	3.78 (59) / 0.47 (89)	0.082 (102, 15)	0.085 (115, 16)	0.504 (78, 9)	0.389 (89, 10)	0.360 (103, 15)
UCLA	3.93 (81) / 0.43 (67)	0.079 (116, 16)	0.082 (127, 18)	0.507 (74, 8)	0.390 (87, 9)	0.380 (113, 16)
Northwestern	3.86 (72) / 0.42 (64)	0.076 (123, 17)	0.088 (104, 15)	0.416 (118, 16)	0.306 (122, 17)	0.400 (118, 17)
Purdue	3.52 (26) / 0.4 (53)	0.072 (132, 18)	0.085 (119, 17)	0.324 (131, 18)	0.126 (134, 18)	0.444 (127, 18)
Note: Value(Nat. Rank, Conf. Rank)

Athlete Rankings

Nebraska - 2025 Athlete Rankings by Target - Max Accel | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	19 (96, 95)	1.87 (92, 93)	0.05 (61, 63)
2	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	18.1 (89, 85)	1.75 (91, 88)	-0.25 (55, 57)
3	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	17.6 (82, 77)	0.88 (77, 79)	0.76 (74, 73)
4	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	17.3 (78, 70)	0.72 (74, 76)	1.53 (85, 85)
5	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	17.3 (78, 92)	1.49 (87, 85)	0.95 (77, 73)
6	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	17.1 (75, 58)	0.49 (68, 66)	1.45 (84, 87)
7	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	16.9 (71, 65)	0 (56, 59)	-0.53 (49, 47)
8	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	16.8 (69, 64)	0.06 (57, 56)	-0.29 (54, 55)
9	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	16.7 (67, 58)	-0.02 (55, 54)	0.18 (64, 69)
10	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	16.5 (63, 52)	-0.23 (49, 50)	-0.15 (57, 57)
11	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	16.3 (58, 62)	0.28 (63, 63)	-2.05 (21, 25)
12	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	16 (52, 59)	-0.43 (42, 48)	-0.25 (55, 57)
13	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	15.7 (45, 51)	-0.57 (38, 45)	-0.39 (52, 55)
14	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	15.7 (45, 29)	-1.08 (22, 20)	-4.15 (3, 2)
15	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	15.6 (43, 15)	-0.86 (29, 24)	-0.01 (60, 57)
16	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	15.4 (39, 50)	-0.29 (47, 49)	1.67 (86, 83)
17	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	15.2 (34, 24)	-1.31 (17, 16)	-3.97 (3, 5)
18	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	15.1 (32, 11)	-1.17 (20, 15)	-1 (39, 37)
19	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	14.7 (25, 8)	-1.48 (13, 11)	-0.33 (53, 56)
20	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	14.4 (21, 49)	-0.97 (26, 30)	-2.48 (15, 20)
21	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	14.3 (20, 7)	-2.47 (3, 4)	-2.94 (10, 9)
22	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	14 (16, 37)	-0.87 (28, 34)	-2.7 (12, 17)
23	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	13.9 (15, 61)	0.29 (63, 58)	0.56 (71, 69)
24	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	13.6 (12, 53)	0.12 (59, 53)	0.05 (62, 61)
25	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	13.5 (12, 1)	-3.17 (1, 0)	-2.24 (18, 11)
26	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	13.4 (11, 48)	-0.38 (44, 39)	-1.37 (32, 31)
27	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	13.3 (10, 2)	-2.75 (2, 1)	-2.88 (11, 11)
28	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	13.1 (9, 40)	0.29 (63, 58)	1.96 (89, 86)
29	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	13.1 (9, 40)	-0.52 (39, 34)	-0.38 (52, 53)
30	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	13.1 (9, 20)	-1.18 (19, 26)	0.16 (63, 63)
31	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	12.8 (7, 16)	-1.14 (21, 27)	NA
32	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	12.5 (5, 25)	-0.75 (33, 29)	-1.55 (28, 28)
33	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	12.1 (4, 18)	-1.13 (21, 18)	NA
34	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	12.1 (4, 18)	-1.36 (15, 14)	-3.45 (6, 6)
35	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	11.2 (1, 8)	-1.69 (10, 11)	0.71 (74, 71)

Nebraska - 2025 Athlete Rankings by Target - Max Speed First 10yds | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	19.67 (97, 98)	1.82 (97, 97)	2.09 (94, 95)
2	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	19.09 (94, 91)	0.94 (86, 84)	-0.48 (47, 46)
3	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	18.95 (92, 89)	1.33 (93, 92)	1.66 (91, 90)
4	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	18.84 (90, 81)	0.57 (76, 73)	-0.11 (59, 56)
5	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	18.71 (88, 97)	1.23 (91, 94)	1.1 (85, 88)
6	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	18.65 (87, 87)	0.63 (78, 85)	0.61 (77, 80)
7	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	17.74 (69, 38)	0.18 (61, 54)	-0.23 (55, 59)
8	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	17.54 (66, 42)	-0.71 (24, 20)	-2.26 (11, 7)
9	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	17.34 (61, 30)	-0.17 (46, 39)	0.2 (67, 73)
10	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	16.88 (52, 20)	-0.53 (30, 26)	-0.52 (46, 45)
11	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	16.86 (52, 21)	-0.77 (22, 16)	-1.65 (18, 10)
12	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	16.85 (52, 41)	0.03 (55, 50)	1.76 (92, 93)
13	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	16.84 (51, 97)	1.83 (97, 96)	2.28 (95, 93)
14	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	16.38 (43, 9)	-0.81 (20, 15)	-0.85 (36, 30)
15	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	16.25 (40, 54)	-1.15 (11, 7)	-2.33 (10, 8)
16	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	16.14 (39, 5)	-1.37 (7, 4)	-1.3 (25, 22)
17	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	15.65 (33, 2)	-1.46 (6, 3)	-1.61 (19, 15)
18	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	15.64 (32, 19)	-1.58 (5, 7)	-2.31 (10, 13)
19	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	15.57 (32, 90)	0.58 (76, 79)	1.28 (87, 84)
20	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	15.53 (31, 69)	0.8 (83, 83)	3.06 (98, 97)
21	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	15.47 (31, 9)	-1.15 (11, 13)	-0.91 (34, 34)
22	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	15.18 (28, 16)	-0.65 (26, 23)	-1.53 (20, 17)
23	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	14.8 (24, 82)	0.73 (81, 78)	-2.54 (8, 16)
24	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	14.51 (21, 73)	0.74 (81, 82)	2.61 (96, 95)
25	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	14.41 (21, 70)	-0.05 (51, 52)	-1.74 (17, 13)
26	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	14.36 (20, 77)	0.35 (68, 70)	-1.69 (17, 27)
27	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	14.27 (19, 76)	0.59 (77, 74)	1.32 (88, 80)
28	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	13.84 (16, 70)	0.18 (61, 65)	-1.69 (18, 27)
29	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	13.71 (15, 1)	-3.14 (0, 0)	-5.05 (0, 0)
30	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	13.07 (10, 50)	-0.31 (39, 46)	-1.38 (23, 32)
31	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	12.08 (4, 22)	-0.98 (15, 22)	NA
32	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	11.94 (3, 4)	-1.64 (4, 3)	NA
33	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	11.87 (3, 17)	-1.57 (5, 10)	-1.64 (18, 28)
34	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	11.82 (3, 17)	-1.21 (10, 16)	-3.74 (2, 7)
35	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	10.6 (1, 5)	-2.25 (1, 3)	-3.69 (2, 7)

Nebraska - 2025 Athlete Rankings by Target - Max Speed 10yds 20yds | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	20.87 (96, 89)	0.4 (71, 71)	-0.61 (41, 37)
2	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	20.84 (95, 96)	1.12 (93, 92)	0.88 (82, 83)
3	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	20.53 (92, 98)	1.62 (97, 96)	2.15 (95, 93)
4	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	20.39 (89, 91)	0.92 (89, 87)	-0.31 (51, 51)
5	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	20.1 (84, 64)	0.2 (62, 61)	-0.09 (58, 59)
6	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	20 (82, 60)	0.34 (68, 67)	0.39 (72, 75)
7	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	19.72 (76, 79)	0.7 (82, 81)	2.44 (96, 94)
8	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	19.71 (76, 50)	0.32 (67, 64)	0.24 (68, 74)
9	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	19.5 (71, 56)	-0.15 (44, 47)	-0.57 (42, 37)
10	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	18.9 (57, 33)	-0.65 (22, 17)	-1.55 (17, 9)
11	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	18.7 (52, 8)	-0.79 (18, 11)	0.01 (61, 64)
12	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	18.6 (50, 12)	-0.98 (13, 7)	-1.68 (14, 9)
13	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	18.51 (49, 41)	0.12 (58, 56)	2.07 (95, 95)
14	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	18.5 (48, 80)	-0.61 (24, 29)	-1.17 (25, 26)
15	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	18.43 (47, 38)	-0.49 (29, 30)	-0.93 (30, 35)
16	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	18.38 (46, 5)	-1.12 (10, 5)	-1.86 (12, 7)
17	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	18.29 (45, 13)	-0.68 (21, 16)	-0.57 (42, 36)
18	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	18.05 (41, 24)	-0.43 (32, 34)	0.12 (65, 64)
19	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	17.16 (32, 89)	0.89 (88, 86)	2.34 (96, 93)
20	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	16.9 (30, 50)	0.5 (75, 74)	3.09 (98, 98)
21	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	16.74 (29, 81)	-0.11 (46, 47)	-0.41 (47, 43)
22	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	16.71 (28, 81)	1.03 (91, 88)	2.23 (95, 92)
23	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	16.6 (27, 96)	1.55 (97, 93)	1.54 (91, 85)
24	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	16.4 (25, 20)	-0.58 (25, 31)	-0.25 (53, 50)
25	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	16.2 (24, 68)	-0.34 (36, 40)	-2.13 (9, 11)
26	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	15.55 (18, 47)	-0.13 (46, 46)	NA
27	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	14.96 (14, 72)	0.26 (64, 61)	-0.35 (49, 49)
28	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	14.9 (13, 69)	0.32 (67, 64)	-0.15 (56, 55)
29	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	14.02 (8, 49)	-0.64 (23, 31)	-1.96 (11, 18)
30	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	14 (8, 48)	-0.16 (44, 47)	-1.07 (27, 34)
31	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	13.81 (7, 44)	-0.69 (21, 28)	-3.59 (2, 5)
32	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	13.5 (6, 0)	-3.73 (0, 0)	-5.39 (0, 0)
33	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	13.39 (5, 34)	-0.31 (37, 43)	2.54 (96, 92)
34	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	13.1 (4, 27)	-0.98 (13, 20)	NA
35	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	12.4 (2, 14)	-1.86 (3, 6)	-4.76 (1, 1)

Nebraska - 2025 Athlete Rankings by Target - Max Speed | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	22.1 (100, 99)	1.18 (95, 99)	1.59 (91, 96)
2	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	21.9 (99, 97)	1.1 (94, 98)	1.2 (87, 91)
3	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	21.7 (98, 95)	0.93 (90, 97)	1.31 (88, 93)
4	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	21.4 (93, 83)	0.34 (68, 70)	-0.39 (46, 45)
5	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	21.3 (91, 85)	0.9 (90, 88)	1 (84, 80)
6	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	20.84 (81, 89)	0.29 (65, 66)	-0.55 (41, 46)
7	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	20.7 (77, 60)	0.05 (52, 51)	0.4 (72, 73)
8	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	20.67 (76, 63)	0.48 (75, 68)	-0.54 (42, 38)
9	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	20.53 (73, 96)	1.34 (96, 95)	1.94 (93, 92)
10	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	20.5 (71, 36)	0.05 (51, 46)	0.38 (71, 77)
11	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	20.4 (69, 94)	0.24 (62, 63)	-0.37 (47, 48)
12	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	20.33 (68, 40)	0.17 (58, 59)	0.3 (69, 77)
13	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	20.1 (62, 40)	-0.24 (39, 39)	-0.46 (44, 42)
14	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	19.7 (54, 17)	-0.7 (21, 13)	-1.17 (25, 19)
15	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	19.5 (51, 33)	-0.37 (33, 29)	-0.4 (46, 43)
16	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	19.2 (46, 40)	-0.38 (33, 33)	-0.27 (50, 54)
17	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	18.9 (42, 5)	-1.52 (6, 4)	-1.25 (23, 18)
18	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	18.6 (38, 25)	-0.73 (20, 20)	2.23 (95, 96)
19	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	17.8 (31, 50)	0.7 (83, 81)	3.79 (99, 98)
20	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	17.7 (30, 84)	0.99 (91, 86)	2.72 (97, 95)
21	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	17.6 (29, 27)	-0.34 (34, 41)	-0.07 (57, 56)
22	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	16.9 (24, 95)	1.4 (97, 93)	1.14 (86, 79)
23	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	16.74 (23, 67)	-0.63 (23, 29)	-0.82 (33, 34)
24	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	16.71 (23, 67)	0.6 (79, 76)	2.11 (94, 90)
25	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	16.6 (22, 62)	-0.73 (20, 27)	-2.45 (7, 11)
26	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	16.02 (17, 78)	0.94 (90, 82)	1.6 (91, 85)
27	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	15.55 (14, 32)	-0.42 (31, 36)	NA
28	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	15.4 (13, 1)	-2.92 (1, 1)	-4.42 (1, 0)
29	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	15 (10, 53)	0.16 (58, 58)	-0.04 (58, 58)
30	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	14.96 (10, 53)	-0.33 (35, 42)	-1.11 (26, 35)
31	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	14.8 (9, 48)	-0.64 (23, 31)	-2.61 (6, 12)
32	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	14.4 (7, 39)	-0.24 (39, 44)	-1.25 (23, 32)
33	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	14.36 (7, 38)	-0.87 (17, 25)	-1.81 (13, 23)
34	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	13.81 (5, 28)	-1.13 (11, 18)	-3.43 (2, 6)
35	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	13.6 (4, 24)	-1.41 (7, 13)	NA

Nebraska - 2025 Athlete Rankings by Target - Top 3 Avg Speed | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	21.37 (99, 96)	0.79 (89, 95)	1.08 (87, 91)
2	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	21.27 (97, 93)	0.43 (75, 77)	-0.18 (56, 56)
3	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	20.97 (93, 84)	0.72 (87, 90)	0.44 (75, 74)
4	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	20.83 (90, 85)	0.83 (90, 82)	0.91 (84, 76)
5	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	20.73 (88, 69)	0.49 (78, 82)	0.24 (70, 75)
6	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	20.55 (85, 78)	0.59 (82, 73)	-0.6 (41, 38)
7	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	20.5 (83, 92)	0.57 (82, 83)	0.18 (68, 75)
8	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	20.1 (75, 97)	0.17 (60, 62)	-0.11 (59, 58)
9	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	19.9 (71, 50)	-0.16 (43, 43)	-0.67 (38, 33)
10	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	19.75 (67, 35)	0.03 (53, 52)	-0.14 (58, 65)
11	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	19.68 (66, 27)	-0.05 (48, 43)	0.51 (77, 82)
12	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	19.51 (62, 38)	-0.44 (30, 28)	-0.24 (54, 50)
13	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	19.39 (59, 92)	0.71 (87, 86)	0.91 (84, 80)
14	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	19.07 (53, 40)	-0.16 (43, 40)	0.01 (62, 54)
15	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	18.94 (51, 10)	-0.84 (16, 9)	-1.09 (25, 22)
16	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	18.52 (44, 34)	-0.52 (27, 26)	2.51 (97, 98)
17	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	18.44 (43, 33)	-0.51 (27, 27)	-0.63 (40, 44)
18	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	17.91 (38, 3)	-2 (3, 2)	-1.72 (13, 10)
19	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	17.43 (34, 60)	0.71 (87, 84)	3.47 (99, 99)
20	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	17.11 (31, 32)	-0.31 (35, 40)	-0.13 (58, 57)
21	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	17.09 (31, 91)	0.88 (91, 88)	2.34 (96, 94)
22	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	16.3 (25, 75)	-0.63 (22, 26)	-1.2 (23, 24)
23	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	16.05 (23, 66)	-0.9 (14, 18)	-2.5 (5, 7)
24	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	15.56 (20, 51)	0.38 (72, 70)	2.02 (95, 91)
25	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	15.26 (17, 42)	0.31 (68, 67)	NA
26	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	15.18 (17, 2)	-2.59 (1, 1)	-4.13 (1, 0)
27	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	15.16 (17, 83)	0.42 (75, 73)	0.21 (69, 65)
28	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	14.84 (15, 75)	0.65 (85, 82)	-0.04 (61, 60)
29	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	14.63 (13, 70)	0.45 (77, 74)	0.43 (75, 70)
30	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	14.16 (10, 56)	-0.63 (22, 29)	-1.38 (19, 27)
31	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	14.16 (10, 56)	0.33 (70, 68)	0.33 (72, 68)
32	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	13.54 (7, 38)	-0.89 (14, 21)	-3.19 (3, 5)
33	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	13.3 (6, 33)	-0.83 (16, 23)	-2.4 (6, 11)
34	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	13.22 (5, 31)	-0.32 (35, 40)	1.11 (87, 81)
35	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	12.9 (4, 24)	-1.18 (9, 15)	NA

Nebraska - 2025 Athlete Rankings by Target - Top 5 Avg Speed | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jeremiah Charles	CB	3 / 1	213	-5.3%	Non-Transfer	21.07 (97, 93)	0.35 (71, 70)	-0.32 (52, 52)
2	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	21.05 (97, 93)	0.85 (91, 95)	0.79 (83, 87)
3	Vincent Genatone	RB	4 / 1	127	+4.1%	Non-Transfer	20.46 (88, 84)	0.67 (85, 75)	-0.64 (41, 39)
4	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	20.39 (87, 82)	0.79 (89, 80)	0.93 (84, 77)
5	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	20.36 (86, 75)	0.44 (76, 77)	0.03 (64, 60)
6	Jacob Bower	LB	3 / 1	217	+429.3%	Non-Transfer	20.33 (86, 94)	0.81 (90, 91)	0.68 (81, 87)
7	Donovan Jones	CB	2 / 1	692	+714.1%	Non-Transfer	20.33 (86, 63)	0.3 (68, 66)	-0.09 (60, 62)
8	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	19.64 (72, 53)	-0.23 (39, 39)	-0.95 (30, 24)
9	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	19.49 (69, 95)	-0.13 (45, 48)	-0.65 (40, 40)
10	Derek Branch	S	5 / 1	217	+43.7%	Non-Transfer	19.48 (68, 35)	0.02 (53, 52)	-0.35 (51, 57)
11	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	19.13 (60, 35)	-0.43 (30, 28)	-0.24 (55, 50)
12	Amare Sanders	CB	2 / 1	46	-6.1%	Non-Transfer	18.93 (56, 13)	-0.2 (41, 35)	0.36 (73, 78)
13	Carter Nelson	TE	2 / 1	177	-7.3%	Non-Transfer	18.82 (54, 88)	0.54 (80, 79)	0.52 (77, 75)
14	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	18.69 (52, 12)	-0.74 (19, 12)	-1.04 (28, 26)
15	Kwinten Ives	RB	3 / 1	66	-4.3%	Non-Transfer	18.57 (50, 38)	-0.11 (46, 44)	0.13 (67, 60)
16	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	18.35 (47, 40)	-0.38 (32, 33)	2.27 (96, 97)
17	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	18.01 (43, 30)	-0.58 (25, 26)	-1.05 (27, 30)
18	Malcolm Hartzog Jr.	CB	4 / 3	80	-88%	Non-Transfer	17.34 (36, 3)	-2.27 (2, 2)	-2.06 (9, 8)
19	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	17.18 (35, 63)	0.66 (85, 82)	3.29 (98, 98)
20	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	16.87 (33, 35)	-0.25 (38, 43)	-0.24 (55, 56)
21	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	16.42 (29, 88)	0.67 (85, 82)	1.88 (93, 90)
22	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	16.08 (27, 80)	-0.37 (32, 34)	-0.66 (40, 36)
23	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	15.46 (22, 63)	-1.14 (10, 12)	-2.85 (4, 3)
24	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	15.14 (20, 51)	0.46 (77, 75)	2.19 (95, 93)
25	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	14.59 (16, 2)	-2.72 (1, 1)	-4.54 (1, 0)
26	Elijah Pritchett	OL	4 / 2	545	-15.8%	Lateral (P4)	14.53 (16, 80)	0.65 (85, 82)	0.09 (67, 62)
27	Justin Evans	OL	4 / 2	743	-22%	Non-Transfer	14.46 (15, 78)	0.24 (65, 66)	-0.11 (60, 57)
28	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	14.36 (15, 31)	0.21 (63, 61)	NA
29	Rocco Spindler	OL	5 / 2	704	-15.4%	Lateral (P4)	14.11 (13, 70)	0.28 (67, 67)	0.14 (68, 63)
30	Henry Lutovsky	OL	5 / 3	781	-1.5%	Non-Transfer	13.98 (12, 66)	-0.46 (29, 33)	-1.16 (24, 31)
31	Tyler Knaak	OL	4 / 1	191	+189.4%	Non-Transfer	13.75 (11, 60)	0.5 (79, 77)	1 (85, 80)
32	Jason Maciejczak	OL	3 / 1	90	+3.4%	Non-Transfer	13.27 (8, 44)	-0.8 (17, 22)	-3.03 (3, 5)
33	Gunnar Gottula	OL	3 / 1	388	-32.3%	Non-Transfer	12.68 (5, 28)	-0.88 (15, 20)	-2.46 (6, 10)
34	Teddy Prochazka	OL	5 / 3	273	NA	Non-Transfer	12.38 (4, 23)	-0.98 (12, 18)	NA
35	Sam Sledge	OL	3 / 1	62	+51.2%	Non-Transfer	12.28 (3, 21)	-0.52 (27, 31)	0.68 (81, 75)

Nebraska - 2025 Athlete Rankings by Target - Game Speed 10 | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	1.67 (98, 93)	-0.01 (78, 80)	0 (65, 68)
2	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	1.75 (82, 98)	-0.05 (98, 99)	-0.09 (98, 98)
3	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	1.75 (83, 94)	-0.04 (95, 94)	-0.06 (94, 94)
4	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	1.77 (73, 71)	-0.03 (94, 90)	-0.04 (88, 88)
5	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	1.77 (69, 14)	0.04 (8, 4)	0.09 (3, 1)
6	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	1.8 (54, 3)	0.04 (9, 5)	0.07 (8, 6)
7	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	1.82 (44, 79)	0.03 (14, 18)	NA
8	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	1.84 (33, 21)	0.02 (27, 23)	0 (60, 53)
9	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	1.84 (31, 57)	-0.01 (70, 74)	-0.04 (88, 87)
10	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	1.92 (7, 9)	0.02 (24, 29)	0.01 (47, 49)
11	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	1.96 (2, 40)	0.01 (46, 80)	-0.01 (73, 60)

Nebraska - 2025 Athlete Rankings by Target - Game Speed 40 | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	4.57 (93, 82)	-0.04 (68, 77)	-0.03 (66, 73)
2	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	4.64 (87, 72)	-0.08 (79, 75)	-0.07 (74, 75)
3	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	4.74 (71, 71)	-0.06 (76, 77)	-0.08 (75, 79)
4	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	4.75 (70, 98)	0.02 (49, 54)	NA
5	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	4.81 (60, 59)	-0.04 (68, 66)	-0.11 (81, 82)
6	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	4.82 (59, 89)	-0.17 (95, 94)	-0.43 (99, 98)
7	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	4.82 (58, 27)	0.14 (15, 11)	0.28 (8, 4)
8	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	4.99 (33, 61)	-0.01 (58, 55)	-0.09 (77, 71)
9	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	5.04 (28, 2)	0.22 (6, 2)	0.27 (9, 5)
10	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	5.47 (4, 10)	0.13 (17, 23)	0.03 (51, 53)
11	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	5.53 (3, 20)	0.14 (15, 20)	0.27 (9, 20)

Nebraska - 2025 Athlete Rankings by Target - Game Speed Flying 20 | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	1.85 (87, 65)	-0.03 (66, 58)	-0.02 (62, 61)
2	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	1.88 (81, 76)	-0.02 (63, 72)	-0.02 (62, 71)
3	Heinrich Haarberg	TE	5 / 2	235	+82.2%	Non-Transfer	1.89 (79, 98)	-0.05 (74, 72)	NA
4	Emmett Johnson	RB	4 / 2	646	+47.5%	Non-Transfer	1.92 (72, 71)	-0.05 (77, 74)	-0.08 (81, 82)
5	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	1.95 (63, 54)	0.01 (51, 54)	0 (54, 57)
6	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	1.98 (54, 33)	0.04 (35, 36)	0.13 (18, 13)
7	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	2 (49, 77)	-0.09 (89, 86)	-0.24 (98, 96)
8	Vincent Shavers Jr.	LB	2 / 1	532	+42.2%	Non-Transfer	2.05 (37, 63)	0.01 (52, 52)	-0.03 (67, 61)
9	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	2.12 (24, 4)	0.13 (10, 5)	0.14 (16, 11)
10	Luke Lindenmeyer	TE	4 / 2	681	+76%	Non-Transfer	2.33 (5, 14)	0.11 (15, 18)	0.01 (51, 50)
11	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	2.33 (4, 20)	0.14 (8, 20)	0.25 (4, 20)

Nebraska - 2025 Athlete Rankings by Target - Change Of Direction | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	0.7 (95, 88)	0.16 (92, 92)	0.2 (91, 94)
2	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	0.53 (78, 55)	0.04 (67, 71)	0.12 (81, 85)
3	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	0.52 (76, 52)	-0.06 (38, 40)	-0.02 (53, 55)
4	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	0.47 (68, 84)	0.17 (93, 92)	0.13 (83, 82)
5	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	0.45 (63, 73)	0.06 (75, 72)	-0.09 (39, 39)

Nebraska - 2025 Athlete Rankings by Target - Deceleration | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	DeShon Singleton	S	5 / 3	707	-13.7%	Non-Transfer	0.57 (88, 95)	0.21 (94, 94)	0.16 (82, 82)
2	Jacory Barney Jr.	WR	2 / 1	577	+23.3%	Non-Transfer	0.44 (67, 60)	-0.02 (47, 47)	-0.11 (41, 38)
3	Nyziah Hunter	WR	3 / 1	604	+8.6%	Lateral (P4)	0.25 (33, 25)	-0.11 (24, 26)	-0.33 (10, 8)
4	Ceyair Wright	CB	5 / 3	694	+6.4%	Non-Transfer	0.24 (31, 25)	-0.15 (17, 15)	-0.4 (5, 3)
5	Dane Key	WR	4 / 3	602	-6.1%	Lateral (P4)	0.21 (27, 22)	-0.05 (38, 38)	-0.02 (55, 55)

Nebraska - 2025 Athlete Rankings by Target - Dl Burst | Metrics: Value (Overall%, Position%)
Rank	Player	Pos	Year / Exp	Snaps	Snap Change(%)	Transfer Type	Value	OE Perf	OE Dev
1	Willis McGahee IV	LB	2 / 1	69	-64.1%	Non-Transfer	0.13 (92, 80)	-0.01 (39, 51)	0.01 (76, 72)
2	Dasan McCullough	LB	4 / 3	391	+98.5%	Lateral (P4)	0.11 (76, 46)	-0.02 (24, 30)	NA
3	Williams Nwaneri	DE	2 / 1	486	+1146.2%	Lateral (P4)	0.11 (74, 60)	-0.02 (22, 38)	-0.01 (18, 27)
4	Elijah Jeudy	DT	5 / 2	474	+81.6%	Non-Transfer	0.08 (43, 83)	0.06 (93, 91)	0.01 (73, 75)
5	Keona Davis	DT	3 / 1	379	+241.4%	Non-Transfer	0.08 (43, 83)	0.02 (77, 64)	0.01 (72, 74)
6	Cameron Lenhardt	DT	3 / 2	353	+42.9%	Non-Transfer	0.07 (38, 73)	-0.04 (10, 4)	0 (53, 55)
7	Sua Lefotu	DT	3 / 1	28	NA	Non-Transfer	0.05 (5, 11)	0 (39, 22)	NA
8	Riley Van Poppel	DT	3 / 2	367	+416.9%	Non-Transfer	0.05 (6, 12)	-0.01 (35, 18)	0 (53, 56)

Model Assessment

Example Analysis: Top 3 Average Speed

Below are the model assessment outputs that can be useful to reference if curious about model performance. They are particularly useful when concerned about a given metric’s stability and ability to project performance. The model’s performance can provide insight into how well the model captures the variance in the data and whether projections can be trusted.

First is the variance decomposition table showing the model’s error and ability to capture the outcome variable’s variability.

Variance Metric Descriptions
Metric	Definition
Residual FE RMSE	The root mean square error of the fixed effects only model residuals. Measured in terms of the outcome variable; tells us on average how far off predictions are.
Residual RE RMSE	The root mean square error of the random effects model residuals. Measured in terms of the outcome variable; tells us on average how far off predictions are when accounting for athlete-specific effects.
Shrinkage Ratio	The ratio of the random effects variance from the observed data to the model output. Shows whether the influence of random effects is shrunk towards the population mean. A larger value is better, indicating less shrinkage.
Total Var.	The total variance in the outcome variable.
Explained Var. (Fixed)	The amount of variance explained by the fixed effects in the model.
Athlete Var. (Random)	The amount of variance attributed to differences between athletes (random effects).
Error Var.	The amount of variance attributed to unexplained error/noise.
R sq. Marginal	The proportion of variance explained by the fixed effects alone. With R-squared values, we hope to be as close to 100% as possible, showing we have explained most of the variance in the outcome variable.
R sq. Conditional	The proportion of variance explained by both fixed and random effects.
Intra-Class Correlation	The proportion of total variance that is attributable to differences between athletes. Ideally this value is larger, indicating that athlete-specific effects are important.

Variance Component Summary - Top 3 Avg Speed
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.36	1.40
Residual RE RMSE	1.00	0.92
Shrinkage Ratio	0.57	0.68
Total Var.	6.77	6.77
Explained Var. (Fixed)	4.93	4.82
Athlete Var. (Random)	0.61	0.87
Error Var.	1.25	1.11
R sq. Marginal	72.82	71.20
R sq. Conditional	81.55	83.61
Intra-Class Correlation	32.78	43.85

Second, we see the SHAP beeswarm plots. As mentioned previously, this plot is essentially a representation of the aggregation of all observations’ waterfall plots. This plot shows the impact of different variables at different levels and how they influenced predictions. It allows us to understand how the model predicts and assigns importance to different variables. In the plot, darker shades of blue represent lower values of the given variable (or FALSE for indicator variables). Brighter shades of red represent higher values of the given variable (or TRUE for indicator variables). Grey points, seen in the lagged variables, represent instances of missing data. The position on the x-axis shows the impact on the model’s prediction, which can be interpreted in terms of the outcome variable’s units (ex. mph or seconds). Points to the right indicate a positive impact on the prediction, while points to the left indicate a negative impact.

For example, in the plot we see that current weight has a large range of influence over predictions. We see that the color shades from red, starting on the negative end of the x-axis, to blue, ending on the positive end of the x-axis. This tells us that higher values of weight tend to decrease speed output while lighter weights tend to increase speed output.

The plots are available for both the base and lagged models of all metrics and provide insight into what variables are most influential in predicting performance of a given variable.

Model Assessment Across All Metrics

Variance Component Summary - Max Accel
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.48	1.47
Residual RE RMSE	1.33	1.34
Shrinkage Ratio	0.26	0.23
Total Var.	3.91	3.91
Explained Var. (Fixed)	1.71	1.74
Athlete Var. (Random)	0.25	0.21
Error Var.	1.95	1.96
R sq. Marginal	43.78	44.54
R sq. Conditional	50.01	49.89
Intra-Class Correlation	11.20	9.73

Variance Component Summary - Max Speed First 10yds
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.27	1.27
Residual RE RMSE	0.99	0.96
Shrinkage Ratio	0.50	0.54
Total Var.	4.61	4.61
Explained Var. (Fixed)	3.00	3.01
Athlete Var. (Random)	0.43	0.49
Error Var.	1.20	1.13
R sq. Marginal	64.93	65.18
R sq. Conditional	74.06	75.46
Intra-Class Correlation	26.63	30.11

Variance Component Summary - Max Speed 10yds 20yds
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.28	1.30
Residual RE RMSE	1.00	0.94
Shrinkage Ratio	0.50	0.59
Total Var.	5.59	5.59
Explained Var. (Fixed)	3.96	3.91
Athlete Var. (Random)	0.44	0.59
Error Var.	1.20	1.11
R sq. Marginal	70.84	69.91
R sq. Conditional	78.46	80.15
Intra-Class Correlation	26.67	34.68

Variance Component Summary - Max Speed
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.36	1.40
Residual RE RMSE	1.07	0.98
Shrinkage Ratio	0.49	0.63
Total Var.	6.31	6.31
Explained Var. (Fixed)	4.46	4.35
Athlete Var. (Random)	0.49	0.76
Error Var.	1.38	1.22
R sq. Marginal	70.64	68.90
R sq. Conditional	78.17	80.60
Intra-Class Correlation	26.08	38.22

Variance Component Summary - Top 5 Avg Speed
Metric	Base Value	Lag2 Value
Residual FE RMSE	1.40	1.45
Residual RE RMSE	1.02	0.94
Shrinkage Ratio	0.59	0.69
Total Var.	7.18	7.18
Explained Var. (Fixed)	5.22	5.09
Athlete Var. (Random)	0.68	0.96
Error Var.	1.30	1.16
R sq. Marginal	72.65	70.91
R sq. Conditional	81.85	83.90
Intra-Class Correlation	34.38	45.48

Variance Component Summary - Game Speed 10
Metric	Base Value	Lag2 Value
Residual FE RMSE	0.04	0.04
Residual RE RMSE	0.03	0.03
Shrinkage Ratio	0.69	0.69
Total Var.	0.00	0.00
Explained Var. (Fixed)	0.00	0.00
Athlete Var. (Random)	0.00	0.00
Error Var.	0.00	0.00
R sq. Marginal	59.92	59.94
R sq. Conditional	78.44	78.64
Intra-Class Correlation	46.76	47.09

Variance Component Summary - Game Speed 40
Metric	Base Value	Lag2 Value
Residual FE RMSE	0.16	0.16
Residual RE RMSE	0.11	0.11
Shrinkage Ratio	0.66	0.63
Total Var.	0.07	0.07
Explained Var. (Fixed)	0.04	0.04
Athlete Var. (Random)	0.01	0.01
Error Var.	0.02	0.02
R sq. Marginal	59.50	61.64
R sq. Conditional	76.97	77.04
Intra-Class Correlation	43.68	40.62

Variance Component Summary - Game Speed Flying 20
Metric	Base Value	Lag2 Value
Residual FE RMSE	0.10	0.10
Residual RE RMSE	0.07	0.08
Shrinkage Ratio	0.56	0.48
Total Var.	0.02	0.02
Explained Var. (Fixed)	0.01	0.01
Athlete Var. (Random)	0.00	0.00
Error Var.	0.01	0.01
R sq. Marginal	51.98	54.12
R sq. Conditional	67.79	66.31
Intra-Class Correlation	33.30	26.81

Variance Component Summary - Change Of Direction
Metric	Base Value	Lag2 Value
Residual FE RMSE	0.17	0.16
Residual RE RMSE	0.13	0.13
Shrinkage Ratio	0.49	0.41
Total Var.	0.05	0.05
Explained Var. (Fixed)	0.02	0.02
Athlete Var. (Random)	0.01	0.01
Error Var.	0.02	0.02
R sq. Marginal	41.13	44.17
R sq. Conditional	57.77	56.41
Intra-Class Correlation	28.32	21.98

Variance Component Summary - Deceleration
Metric	Base Value	Lag2 Value
Residual FE RMSE	0.18	0.17
Residual RE RMSE	0.14	0.14
Shrinkage Ratio	0.46	0.39
Total Var.	0.04	0.04
Explained Var. (Fixed)	0.01	0.01
Athlete Var. (Random)	0.01	0.01
Error Var.	0.02	0.02
R sq. Marginal	14.06	27.64
R sq. Conditional	36.40	42.59
Intra-Class Correlation	26.13	20.74