📚 Upside Studies: (1) MLB Study: Analytic & Pitch-Tracking Metrics Associated with UCL Surgery. (2) WNBA Study: Factors Impacting Wins/Losses (3) Golf: Sleep Study.

⚾ Upside Study: Advanced Analytic and Pitch-Tracking Metrics Associated with UCL Surgery in Major League Baseball Pitchers: A Case-Control Study.

🧩 Introduction

Ulnar collateral ligament (UCL) injuries—often resulting in “Tommy John” surgery—have become increasingly common among Major League Baseball (MLB) pitchers, mirroring rising demands for performance and velocity. While previous research has primarily focused on traditional risk factors like pitch count, fastball usage, and velocity, recent advancements in pitch-tracking technology (e.g., Statcast, TrackMan, Hawk-Eye) and advanced analytics (e.g., Stuff+, Location+, Pitching+) offer new dimensions to understanding injury mechanisms.

These modern metrics now play a critical role in MLB’s approach to player development, performance evaluation, and contract negotiations. However, few studies have explored whether these data-driven metrics are linked to increased injury risk, particularly UCL surgeries. This study fills that gap by examining how changes in advanced pitch-tracking and performance metrics correlate with UCL surgery among MLB pitchers between 2018 and 2023.

Authors:

• Michael A. Mastroianni,* MD, Jennifer A. Kunes,* MD, Dany B. El-Najjar,* BS, Kyle K. Obana,* MD, Sohil S. Desai,* MD, Cole R. Morrissette,* MD , Frank J. Alexander,* MS, ATC, Alexander J. Rondon,* MD, David P. Trofa,* MD, Charles A. Popkin,* MD, William N. Levine,* MD, and Christopher S. Ahmad,* y MD Investigation performed at Columbia University Irving Medical Center/NewYork Presbyterian Hospital, New York, New York, USA

You can download the full PDF study by clicking on the button below:

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🧪 Study Overview

• Design: Retrospective case-control study.

• Time frame: April 2018 – November 2023.

• Participants:

  • 117 MLB pitchers who underwent primary UCL surgery (reconstruction or repair).

  • 234 matched controls, matched 2:1 by season, age, position (starter vs. reliever), handedness, and pitch count.

• Data Sources: Publicly available MLB databases including Baseball Savant, FanGraphs, and Brooks Baseball.

• Key Variables:

  • Advanced Analytics: fWAR, xFIP, SIERA, Stuff+, Location+, Pitching+.

  • Pitch-Tracking Metrics: Velocity, spin rate, spin axis, active spin, release point, horizontal/vertical movement, approach angles (HAA/VAA), and release extension.

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📈 Key Findings and Statistical Results

🚨 Risk Factors Significantly Associated with UCL Surgery:

1. Increased Pitch Velocity:

   • UCL surgery group averaged ~1 mph higher velocity than controls in both the season before and the index season.

   • Every 1 mph increase in velocity raised UCL surgery risk by ~20% (aOR = 1.20; P = .03).

2. Superior Command and Pitching Ability:

   • Pitchers who had UCL surgery scored significantly higher in:

     • Pitching+: A composite metric of overall pitching skill.

     • Location+: A command metric measuring pitch placement precision.

     • fWAR: Wins above replacement (value to team).

     • xFIP/SIERA: ERA estimators accounting for fielding, park factors, etc.

   • Higher Location+ was significantly linked to UCL surgery (aOR = 1.11; P = .02).

3. Decreased Fastball Usage:

   • Surprisingly, pitchers who underwent UCL surgery threw fewer fastballs than controls.

   • Lower fastball usage was associated with increased risk (aOR = 0.07; P = .05).

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🧪 No Significant Differences Found In:

Despite popular speculation, several pitch metrics were not associated with UCL injury risk, including:

• Spin-related metrics:

  • Spin rate, active spin %, and spin axis.

• Pitch movement:

  • Horizontal and vertical movement.

• Release mechanics:

  • Horizontal/vertical release points, approach angles (HAA, VAA), and release extension.

• Pitch counts and innings pitched: No statistical difference between cases and controls.

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📉 Rapid Performance Gains May Raise Risk

• Pitchers who rapidly improved their Pitching+ scores over consecutive seasons were more likely to undergo UCL surgery.

• Indicates that aggressive performance improvement—common in offseason training programs—may contribute to injury.

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🧠 Implications for MLB Teams, Coaches, and Players

This study presents a paradox: the very traits that define elite pitchers—velocity, command, and overall performance—may also increase injury risk.

Key implications:

• Velocity is still king, but comes at a price. Increased velocity is a clear, repeatable predictor of UCL injuries.

• Better command (Location+) and higher performance (Pitching+) were also correlated with UCL surgery—possibly due to the mechanical stress required to maintain this consistency.

• Advanced analytics should be integrated not just for scouting talent, but also to monitor risk, especially during periods of rapid improvement.

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📌 Recommendations

For teams and performance staff:

• Monitor sharp offseason improvements in Pitching+ and Location+.

• Avoid overemphasis on velocity increases or mechanical tweaks that overload the elbow.

• Invest in real-time biomechanical tracking (e.g., from Hawk-Eye data) to develop injury-prevention protocols tied to command and release patterns.

For startups and analytics vendors:

• Incorporate injury risk forecasting tools based on multi-season changes in advanced metrics.

• Help teams set safe improvement thresholds to guide player development.

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⚠️ Limitations

• Public injury data excludes pitchers with non-surgical UCL injuries, possibly underestimating total risk.

• Excludes data from rookie seasons or pitchers without two seasons of prior data.

• Cannot account for pitches thrown in bullpen sessions, warm-ups, or training.

• Clinical implications of findings still need biomechanical validation.

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✅ Conclusion

In the largest study of its kind to date, MLB pitchers who underwent UCL surgery:

• Threw harder,

• Used fewer fastballs, and

• Showed superior command and performance metrics (Pitching+ and Location+).

These results suggest that elite performance traits may carry higher biomechanical stress loads, contributing to injury risk. As MLB increasingly embraces data-driven development, integrating injury risk metrics alongside performance metrics will be critical to sustaining player health and career longevity.

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🏀 Upside Study: Data Variables Related to Wins and Losses of Games in Different Scenarios in the WNBA 2023 Season

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📘 Introduction

The Women’s National Basketball Association (WNBA) represents the highest competitive tier of women’s basketball globally, yet research into performance analytics in this league remains underdeveloped compared to the NBA and other men’s leagues. This study aims to address that gap by examining the statistical variables most strongly associated with winning and losing outcomes across different game scenarios in the 2023 WNBA season.

The researchers focused on how contextual factors, such as game location (home vs. away) and game competitiveness (balanced vs. unbalanced), influence which performance indicators best predict success. Recognizing that game dynamics vary significantly based on venue, score margin, and game tempo, the study aimed to offer situationally nuanced insights that coaches and analysts can use to inform tactical decisions and player development strategies.

The novelty of this research lies in its methodological rigor—combining clustering, chi-square tests, and logistic regression—to isolate and compare key variables across four distinct game scenarios. In doing so, it contributes a unique lens through which to evaluate elite women's basketball performance and closes a critical gap in gender-equitable sports science.

Authors:

• Yuxuan Deng, Binjie Luo, Fanchao Lin, Xiaofei Xu & Miguel Ángel Gómez Ruano

Published online: Sept 16, 2023

You can download the full PDF study by clicking on the button below:

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🔬 Methods

📊 Dataset

• Sample size: 249 WNBA games (240 regular season + 9 postseason, excluding overtime).

• Data source: Publicly available data from basketball-reference.com, including 27 variables (14 basic, 13 advanced).

• Examples of basic indicators: FG, FGA, FT, 3P, TRB, AST, STL, TOV, PF.

• Advanced metrics: TS%, eFG%, 3PAr, FTr, ORB%, DRB%, TRB%, Ortg, DRtg, etc.

⚙️ Analytical Approach

1. Chi-square tests were used to verify the statistical significance of differences in outcomes between home and away games.

2. K-means clustering classified games as either "balanced" (≤12-point differential) or "unbalanced" (>12 points), aligning with prior basketball analytics research.

3. Stepwise logistic regression was applied within each of the four game groups to identify the variables most strongly associated with winning or losing.

4. R statistical software (v4.3.1) was used to conduct all analyses.

🧩 Game Scenario Groups:

1. Home Balanced Games (n = 148)

2. Home Unbalanced Games (n = 101)

3. Away Balanced Games (n = 148)

4. Away Unbalanced Games (n = 101)

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📈 Key Findings by Game Type

🏠 1. Home Balanced Games

Positive correlations with winning:

• Field Goals Made (FG)

• 3-Point Field Goals Made (3P)

• Free Throws Made (FT)

• Total Rebounds (TRB)

• Steals (STL)

Negative correlations:

• Field Goal Attempts (FGA)

• Turnovers (TOV)

• Personal Fouls (PF)

📌 Interpretation: Success in close home games hinges on efficiency and discipline. Winning teams made more shots but attempted fewer, rebounded more, and committed fewer mistakes.

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🏠 2. Home Unbalanced Games

Positive indicators:

• FG

• Offensive Rebounds (ORB)

• STL

• Blocks (BLK)

Negative indicators:

• FGA

• TOV

📌 Interpretation: In dominant home wins, teams displayed strong inside presence (offensive boards, blocks) and defensive pressure (steals), enabling extended possessions and denying opponent scoring chances.

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🛫 3. Away Balanced Games

Positive indicators:

• FG

• FT

• TRB

• AST

• STL

Negative indicators:

• FGA

• TOV

• PF

📌 Interpretation: Winning on the road in close games requires efficient offense, ball sharing, and composure under pressure. Assists played a unique role, suggesting team cohesion is crucial in hostile environments.

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🛫 4. Away Unbalanced Games

Positive indicators:

• 3P

• Offensive Rebound Percentage (ORB%)

• Defensive Rebound Percentage (DRB%)

• Total Rebound Percentage (TRB%)

📌 Interpretation: In blowout away wins, perimeter scoring and dominance on the glass were decisive. These wins were driven by a combination of long-range accuracy and controlling possession through rebounding.

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🧠 Discussion and Thematic Insights

🔄 Common Trends Across Groups

• Field Goals Made (FG) consistently had a positive influence.

• Field Goal Attempts (FGA) and Turnovers (TOV) showed a negative association, emphasizing the importance of quality over quantity in offensive execution.

• Steals (STL) correlated positively with winning across most groups, reinforcing the value of defensive disruption.

• Personal Fouls (PF) consistently harmed win probability in tight games, likely due to increased free throw opportunities and reduced time for star players.

🎯 Contextual Differentiators

• Assists (AST) mattered only in away balanced games, suggesting that cohesive, pass-heavy play counters away-game adversity.

• Blocks (BLK) and Offensive Rebounds (ORB) were more influential in home unbalanced games, highlighting the confidence and energy advantage provided by home crowds.

• Rebounding percentages (ORB%, DRB%, TRB%) emerged only in away unbalanced games, highlighting their importance in dominating games on the road.

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🧩 Strengths and Contributions

1. Contextual Game Segmentation: The study’s division of games into balanced/unbalanced and home/away enabled highly granular performance insights.

2. Combination of Traditional and Advanced Metrics: Both raw stats (like FG and STL) and modern efficiency indicators (e.g., ORB%) were used.

3. Focus on Elite Women's Basketball: Fills a gap in performance analysis for the WNBA, contributing to more equitable data-informed coaching.

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⚠️ Limitations and Future Recommendations

• The study did not explore psychological or external environmental factors (e.g., travel fatigue, audience impact).

• The single-season dataset limits the generalizability of findings; longitudinal studies are recommended.

• Future research could incorporate player tracking data and team line-up analytics to build more dynamic predictive models.

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✅ Conclusion

This study provides a robust statistical framework to understand what variables drive wins and losses in the WNBA under varying game scenarios. The findings emphasize the importance of shooting efficiency, rebounding, disciplined defense, and context-aware team strategy. The study’s insights are especially valuable for WNBA coaches, analysts, and performance staff, who can use these indicators to better plan for different game conditions.

Ultimately, this research not only advances sports analytics in women’s basketball, but also sets a foundation for future studies that aim to integrate data science with coaching strategy in elite women’s sports globally.

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⛳ Upside Study: Associations Between Wearable-Derived Sleep and Physiological Metrics With Performance in Professional Golfers

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🧠 Introduction

In elite professional golf, where championships are routinely decided by a single stroke, performance hinges on marginal gains. Unlike other physically intensive sports, golf requires sustained cognitive focus, emotional control, motor precision, and resilience across multiple days of competition. The demands of tournament golf place a premium on recovery and physiological readiness—domains where sleep quality and autonomic nervous system function (reflected in metrics such as resting heart rate [RHR] and heart rate variability [HRV]) are increasingly viewed as critical.

Despite a growing ecosystem of wearable technologies and performance monitoring tools, little empirical research has been conducted on how these physiological variables impact real-world golf performance at the professional level. This study bridges that gap, providing a large-scale, longitudinal analysis of nearly 400 tour-level golfers to explore how sleep metrics, biometric data, and a composite Recovery score relate to competition outcomes like score, strokes gained, and shot quality.

Authors:

• Gregory J. Grosicki1 , William von Hippel1,2, Finnbar Fielding1 , Jeongeun Kim1 9 , Christopher Chapman1 , Kristen E. Holmes1 10 11 12 1 13 Performance Science, WHOOP Inc., Boston, Massachusetts, United States 2 14 Research with Impact, Brisbane, Queensland, Australia

• Published: April 2025 | Affiliation: WHOOP Inc.

You can download the full PDF study by clicking on the button below:

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📊 Study Design & Methods

🧪 Participants and Data

• 389 professional golfers (mean age: 34.1 years)

• 521 elite-level competitive events between 2017 and 2025

• 35,140 nights of biometric and sleep data

• Performance data sourced from DataGolf, including advanced metrics like:

  • Total score

  • Strokes Gained (overall, putting, tee-to-green)

  • Great Shots (≥0.5 strokes gained per shot)

  • Poor Shots (≥0.5 strokes lost per shot)

📍 Wearable Metrics Collected via WHOOP:

• Sleep Duration

• Sleep Consistency (regularity of bed and wake times)

• Heart Rate Variability (HRV)

• Resting Heart Rate (RHR)

• Recovery Score: WHOOP’s proprietary readiness score incorporating sleep and physiological metrics

📈 Analytical Approach

• Linear mixed-effects models used to examine both:

  • Between-person differences (e.g., golfers with better average metrics)

  • Within-person changes (e.g., season-to-season improvements)

• Controlled for age, height, and weight

• Variables categorized into tertiles for deeper group-level insights

• Cohen’s d used to evaluate effect sizes

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💡 Key Findings & Insights

🛌 1. Sleep Metrics Strongly Predict Golf Performance

• Golfers with longer and more consistent sleep outperformed peers:

  • +1 hour of sleep → 0.52 stroke improvement per round (b = -0.522, β = -0.185)

  • +10% sleep consistency → 0.38 stroke improvement (b = -0.382, β = -0.134)

• High sleep tertile athletes gained more strokes and had better putting outcomes

• However, within-person increases in sleep duration did not lead to improvements, suggesting sleep duration benefits may plateau at already high levels (~7.2 hrs/night)

❤️ 2. Cardiac Autonomic Indicators Matter – Especially RHR

• Lower RHR consistently linked to improved performance:

  • Especially significant in tee-to-green strokes gained (Cohen’s d = 0.232)

  • Suggests better cardiovascular fitness and recovery capacity

• HRV was more complex:

  • No strong between-person association

  • But within-person increases in HRV were linked to better scores and more strokes gained

  • Indicates HRV may be a better dynamic, individual-level readiness indicator

🔄 3. Recovery Score Is the Most Reliable Predictor

• WHOOP’s Recovery Score showed the strongest and most consistent correlations with performance:

  • +10% Recovery → 0.48 stroke improvement (b = -0.476, β = -0.232)

  • Higher Recovery linked to:

    • Lower total scores

    • More great shots

    • Fewer poor shots

    • Better putting and tee-to-green performance

• Within-person improvements in Recovery were statistically significant and practically meaningful (Cohen’s d = 0.12–0.41)

🔄 4. Improvements Within Individuals Yield Gains

• When athletes improved their:

  • Sleep consistency

  • HRV

  • Recovery Score
    — they experienced measurable performance boosts:

    • Lower round scores

    • Better stroke gains

    • Fewer mistakes

• These trends support the importance of tracking readiness and recovery over time, not just comparing across athletes

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📉 Statistical Summary Table

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🧠 Discussion

This landmark study shows that biologically driven factors like sleep and autonomic balance are tightly connected to competitive golf outcomes—even in a sport dominated by skill and psychology. Golfers who maintained high-quality, regular sleep and low RHRs outperformed their peers across key metrics, from lower total strokes to better shot efficiency.

Importantly, the study also demonstrates that seasonal improvements within the same athlete (not just group averages) forecast performance gains. This has strong implications for coaches, sports scientists, and athletes themselves: continuous physiological monitoring, guided by high-quality wearables, can provide actionable feedback for improving competitive readiness.

Recovery Score, as a multivariate, adaptive metric, appears to provide the most holistic assessment of athlete readiness and may be a valuable tool in managing training loads and optimizing tournament preparation.

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⚠️ Limitations

• Observational design: Cannot confirm causality; unmeasured variables like nutrition, stress, or travel may confound results.

• Single-device data source (WHOOP): Although validated, generalizability to other wearables is limited.

• Elite athlete sample: Results may not translate to amateurs or recreational golfers.

• Sleep duration already high: May explain lack of effect from sleep extension.

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✅ Conclusion

This study provides the strongest evidence to date that wearable-derived physiological data is associated with performance in professional golf. The key takeaways are:

• Longer, more consistent sleep improves competitive performance.

• Lower resting heart rate and higher HRV (especially within individuals) signal better readiness.

• Recovery Score, combining multiple indicators, offers the most consistent and predictive value.

As elite sports continue to pursue marginal gains, integrating objective biometric monitoring into coaching and preparation may be a game-changer—not just in golf, but across precision and endurance sports alike.

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