Contents

Player Profile

Metric Value
Date of Birth 01/03/2008
Height 185 cm
Weight 80.1 Kg
Position CB
Club Hellenic FC

1. Data Key & Methodology

Data Collected

This analysis utilizes the following metrics collected via StatSports GPS/HR trackers and match logs:

Table 1: Key Metrics Collected
Category Metric Description
Match Info Event/Game Type Competition or type of match
Result Win (W), Draw (D), or Loss (L)
Mins Played Duration played in the match
GPS Metrics KM Run Total distance covered in kilometers
Max Speed (KPH) Highest speed reached during the match
No. Sprints Number of high-intensity sprints performed (> 19.8 km/h)
Accel Number of accelerations
Decels Number of decelerations
HSR High-Speed Running: Distance covered above 19.8 km/h
HID High-Intensity Distance: Similar to HSR, specific definition depends on tracker
DPM Dynamic Player Movement or similar intensity metric from tracker
HR Metrics Max HR (BPM) Maximum heart rate reached
AVG HR (BPM) Average heart rate during the match
>85% HR (minutes) Time spent above 85% of maximum heart rate (‘Red Zone’)
Derived Metrics Strain Subjective rating of perceived exertion/load

Data Limitations & Modeling

  • Small Sample Size: The primary limitation is the small number of games (54 total), especially for the measured heart-rate data.
  • Single Season/Format: Data covers one season.
  • Tracker Variability: GPS/HR data has inherent small margins of error.
  • Modeling: A k-Nearest Neighbors (KNN) model estimated missing HR values (Figure 3) based on physical metrics (normalized where appropriate) and strain from games with complete data (using k=3 neighbors). “Composite” values blend measured data with these estimates.
  • Tactical Variability: Player statistics span over 3 teams with different playing styles creating variability in physiological and match statistics and results.

2. Executive Summary

This document presents a data-driven analysis of Levi Jocum’s 2025 season. The combination of match statistics and StatSports tracking data demonstrates Levi is a player defined by physical capacity, high-impact defensive actions, and a superior cardiovascular engine. This report quantifies specific attributes, benchmarked against elite academy standards.

3. Physical Output & Work Rate

Levi’s season data shows consistent physical output (normalized for playing time).

Normalized Work Rate (Meters per Minute)

Over the course of the season, Levi played in games of various durations. To enable fair comparison, distance covered per minute played (m/min) was calculated, reflecting intensity.

Figure 1: Distribution of Work Rate (Meters per Minute).

Figure 1: Distribution of Work Rate (Meters per Minute).

The distribution shows Levi consistently operates at a high intensity, averaging 80.9 m/min. Elite U18 academy defenders typically average 105-120 m/min (Varley et al., 2012; Malone et al., 2017). His peak work rate of 115.2 m/min falls within this range, showing he can reach elite intensity.

Peak Performance Metrics & Benchmarks

The table highlights Levi’s peak outputs.

Table 2: Season’s Best Performance Metrics
Metric Value
Most Distance Covered (km) 10.20
Highest Work Rate (m/min) 115.20
Highest Max Speed (km/h) 31.20
Most Sprints in a Game 33.00
Highest HSR (m) 331.00
Peak Heart Rate (bpm) 206.00
Longest Time in Red Zone (min) 42.85
  • Endurance: Levi’s peak distance of 10.2 km meets the 9.5-10.5 km range for elite U18 Centre-Backs (Ade et al., 2016).
  • Speed: His Max Speed of 29.5 km/h approaches the 30-34 km/h range for academy defenders (Haugen et al., 2014).

Environmental Context: Cape Town Climate

Pre-season friendlies were played during the hot Cape Town summer. Playing extended minutes (105 mins) in heat likely contributed to higher physiological strain, making his endurance figures commendable.

4. Full-Season Physiological Profile (Composite)

To gain a complete picture, a composite profile was created, combining measured StatSports data with KNN modeled estimates. The chart shows the average “Time in the Red Zone” by game type.

Figure 2: Full-season composite 'Red Zone' profile by game type.

Figure 2: Full-season composite ‘Red Zone’ profile by game type.

5. Cardiovascular Engine (Measured Data)

This section focuses only on the verified, measured HR data.

Benchmark Context

Elite youth players typically sustain an average heart rate of 85-90% of their maximum, often averaging 170-185 BPM.

Heart Rate Analysis

Levi’s data aligns perfectly. In measured high-intensity matches, his Average HR of 170 BPM and Peak HR of 206 BPM demonstrate an elite cardiovascular system.

Figure 3: Heart rate data from select high-intensity measured matches.

Figure 3: Heart rate data from select high-intensity measured matches.

Time in the “Red Zone”

Elite players often spend 60-75% of match time above 85% HRmax (Akubat et al., 2014). Levi’s measured data shows significant time in this zone.

Figure 4: Measured time spent in the maximum effort 'Red Zone'.

Figure 4: Measured time spent in the maximum effort ‘Red Zone’.

6. Performance by Game Type

Performance and physical output differ between formats. This section compares normalized intensity metrics.

Figure 5a: Average Work Rate (m/min) across game types.

Figure 5a: Average Work Rate (m/min) across game types.

Figure 5b: Average High-Speed Running Intensity (m/min) across game types.

Figure 5b: Average High-Speed Running Intensity (m/min) across game types.

Figure 5c: Average Sprint Intensity (sprints/min) across game types.

Figure 5c: Average Sprint Intensity (sprints/min) across game types.

When normalized per minute:

This is expected due to the increased intensity of tournament football (shorter game duration and mostly long ball tactics).

Results by Game Type

The table summarizes team performance and Levi’s contribution in each format.

Table 3: Team Results & Key Performance Metrics by Game Type
game_type games_played mtotal_mins_played cleansheet_percentage goal_difference win_percentage
Friendly 9 696 33% 12 74%
League 23 1613 35% 68 56%
Competition 22 1094 32% 23 45%

This summary provides a clear view of performance across different formats. Notably, cleansheet percentage remained consistent across different formats.

7. Defensive Impact & Workload

Understanding impact involves analyzing style and effort.

Part A: The Defensive “Fingerprint”

Levi exhibits a balanced defensive profile, almost evenly split between proactive Accelerations, reactive Decelerations, and Sprints, indicating versatility. The labels clearly show the percentage contribution and name of each action type.

Figure 6a: A profile of total defensive actions, showing a balanced 'defensive fingerprint'.

Figure 6a: A profile of total defensive actions, showing a balanced ‘defensive fingerprint’.

Part B: Workload Under Pressure

Total defensive actions increase significantly with match difficulty, peaking in “High” and “Very High” strain games. This shows Levi’s work rate rises to meet the challenge.

Figure 6b: A comparison of total defensive workload against the perceived strain of the match.

Figure 6b: A comparison of total defensive workload against the perceived strain of the match.

8. Conclusion: Player Profile

References: