How do physical traits, such as weight and height, correlate with
UFC fighters’ fighting capabilities?
Research Question:
The UFC_stats dataset comprises 1673 entries, each representing a
unique fighter, and includes 14 columns of relevant attributes. Here’s a
brief description of the key columns used in this analysis:
fighter_name: The first and last name of the fighter, serving as a
unique identifier.
Height: The fighter’s height in inches, a fundamental physical
attribute influencing reach and leverage during combat.
Weight: The weight in pounds of the fighter, which impacts strength,
speed, and endurance.
Reach: The wingspan in inches of the fighter, affecting striking
range and defensive capabilities.
Stance: Indicates the fighter’s preferred orientation during combat,
such as orthodox (right-handed) or southpaw (left-handed).
Str_Acc: Striking accuracy percentage, representing the proportion
of strikes landed successfully out of those attempted.
TD_Avg: Takedown average per fight, indicating the fighter’s
proficiency in executing takedowns.
TD_Acc: Takedown accuracy percentage, reflecting the effectiveness
of the fighter’s takedown attempts.
Sub_Avg: Submission average, denoting the frequency of successful
submission maneuvers per fight.
library(tidyverse)
## Warning: package 'ggplot2' was built under R version 4.3.3
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## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
UFC_stats <- read.csv("UFC_stats (1).csv")
# Clean the dataset
clean_UFC_stats <- UFC_stats %>%
filter(!is.na(Height) & !is.na(Weight) & !is.na(Reach) & !is.na(Str_Acc) & !is.na(TD_Avg) & !is.na(TD_Acc) & !is.na(Sub_Avg))
# Summary
summary(clean_UFC_stats)
## X fighter_name Height Weight
## Min. : 1.0 Length:1672 Min. :60.00 Min. :115.0
## 1st Qu.: 418.8 Class :character 1st Qu.:67.75 1st Qu.:135.0
## Median : 837.5 Mode :character Median :70.00 Median :155.0
## Mean : 837.2 Mean :70.07 Mean :164.6
## 3rd Qu.:1255.2 3rd Qu.:73.00 3rd Qu.:185.0
## Max. :1673.0 Max. :83.00 Max. :265.0
##
## Reach Stance Birthyear SLpM
## Min. :58.00 Length:1672 Min. :1963 Min. : 0.130
## 1st Qu.:69.00 Class :character 1st Qu.:1982 1st Qu.: 2.280
## Median :72.00 Mode :character Median :1987 Median : 3.155
## Mean :71.82 Mean :1986 Mean : 3.317
## 3rd Qu.:75.00 3rd Qu.:1990 3rd Qu.: 4.072
## Max. :84.00 Max. :1999 Max. :19.910
## NA's :3
## Str_Acc SApM Str_Def TD_Avg
## Min. : 8.00 Min. : 0.100 Min. : 4.00 Min. : 0.000
## 1st Qu.:38.00 1st Qu.: 2.480 1st Qu.:48.00 1st Qu.: 0.440
## Median :44.00 Median : 3.200 Median :55.00 Median : 1.180
## Mean :44.31 Mean : 3.505 Mean :53.75 Mean : 1.548
## 3rd Qu.:50.00 3rd Qu.: 4.150 3rd Qu.:60.00 3rd Qu.: 2.303
## Max. :88.00 Max. :21.180 Max. :86.00 Max. :18.000
##
## TD_Acc TD_Def Sub_Avg
## Min. : 0.00 Min. : 0.00 Min. : 0.0000
## 1st Qu.: 20.00 1st Qu.: 39.00 1st Qu.: 0.0000
## Median : 36.00 Median : 60.00 Median : 0.4000
## Mean : 35.83 Mean : 54.96 Mean : 0.6602
## 3rd Qu.: 50.00 3rd Qu.: 75.00 3rd Qu.: 0.9000
## Max. :100.00 Max. :100.00 Max. :20.4000
##
# Mean values for key variables
mean_values <- clean_UFC_stats %>%
select(Height, Weight, Reach, Str_Acc, TD_Avg, TD_Acc, Sub_Avg) %>%
summarise_all(mean)
mean_values
## Height Weight Reach Str_Acc TD_Avg TD_Acc Sub_Avg
## 1 70.07057 164.6376 71.82177 44.30682 1.547853 35.83014 0.6602273
# Maximum values for key variables
max_values <- clean_UFC_stats %>%
select(Height, Weight, Reach, Str_Acc, TD_Avg, TD_Acc, Sub_Avg) %>%
summarise_all(max)
max_values
## Height Weight Reach Str_Acc TD_Avg TD_Acc Sub_Avg
## 1 83 265 84 88 18 100 20.4
Linear Regression
linear regression model to analyze the relationship between physical
traits (height, weight, reach) and fighting capabilities (striking
accuracy, takedown proficiency, submission average) among UFC
fighters
# Create a linear regression model
lm_model <- lm(Str_Acc ~ Height + Weight + Reach + TD_Avg + TD_Acc + Sub_Avg, data = clean_UFC_stats)
# Summarize the linear regression model
summary(lm_model)
##
## Call:
## lm(formula = Str_Acc ~ Height + Weight + Reach + TD_Avg + TD_Acc +
## Sub_Avg, data = clean_UFC_stats)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.910 -5.608 -0.333 5.247 40.758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.88642 6.08095 3.435 0.000608 ***
## Height 0.43506 0.15229 2.857 0.004332 **
## Weight 0.03364 0.01090 3.086 0.002062 **
## Reach -0.21705 0.11901 -1.824 0.068357 .
## TD_Avg 1.24550 0.16697 7.460 1.39e-13 ***
## TD_Acc 0.02192 0.01022 2.144 0.032162 *
## Sub_Avg 0.41334 0.20995 1.969 0.049154 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.17 on 1665 degrees of freedom
## Multiple R-squared: 0.09764, Adjusted R-squared: 0.09439
## F-statistic: 30.03 on 6 and 1665 DF, p-value: < 2.2e-16
In this data analysis, I first cleaned the dataset by removing any
rows with missing values for key variables. Then, I calculated summary
statistics including mean and maximum values for each variable.
In addition to that there is a statistically significant
relationship between the Reach and Striking Accuracy variables. Since
the p-value is less than 0.05, you can conclude that the relationship
observed in your data is unlikely to be due to random chance alone.
Finally, I created scatter plots to visualize the relationships
between physical traits (height, weight, reach) and fighting
capabilities (striking accuracy, takedown proficiency, submission
average). These scatter plots will provide insights into any potential
correlations between the variables and help address the research
question regarding the influence of physical traits on fighting
capabilities.
# Scatter plot: Height vs. Weight
ggplot(clean_UFC_stats, aes(x = Height, y = Weight)) +
geom_point() +
labs(title = "Height vs. Weight",
x = "Height (inches)",
y = "Weight (lbs)") +
theme_minimal()
# Scatter plot: Reach vs. Striking Accuracy with a linear regression line
ggplot(clean_UFC_stats, aes(x = Reach, y = Str_Acc)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") + # Adding linear regression line
labs(title = "Reach vs. Striking Accuracy",
x = "Reach (inches)",
y = "Striking Accuracy (%)") +
theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
# Scatter plot: Height vs. Takedown Average
ggplot(clean_UFC_stats, aes(x = Height, y = TD_Avg)) +
geom_point() +
labs(title = "Height vs. Takedown Average",
x = "Height (inches)",
y = "Takedown Average") +
theme_minimal()
Conclusion and Future Directions
In conclusion, the analysis revealed several insights into the
relationship between physical traits and fighting capabilities among UFC
fighters. I found that while there is a wide range of physical
attributes among fighters, including height, weight, and reach, these
factors exhibit varying degrees of correlation with performance measures
such as striking accuracy, takedown proficiency, and submission average.
Specifically, there appears to be a positive correlation between reach
and striking accuracy, indicating that fighters with longer reaches may
have an advantage in landing strikes effectively. However, the
relationships between other physical traits and fighting capabilities
are less clear and may require further investigation.
Where I got this dataset from: SCORE Sports Data Repository