📚 Upside Studies: (1) Basketball Study: Injury Incidence Rate (2) NHL Study: Injury Rates Increase During Olympics? (3) NFL Study: Spending on Star Players & Team Success in the NFL

🏀 Upside Study (1): Injury Incidence Rate According to Mechanism, Body Location, and Type in Basketball Players — A Systematic Review and Meta-Analysis

Authors: Emilija Stojanović, Aaron T. Scanlan, Dragan Radovanović, et al.
Published in: Sports Medicine, 2025

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🔎 Introduction

Basketball is a high-intensity sport requiring repeated accelerations, jumps, rapid changes of direction, and constant player contact. These intense physical requirements place basketball athletes at heightened risk of injury, especially at elite and competitive levels. To inform injury prevention strategies, a detailed understanding of injury origins—how players are hurt, what body parts are affected, and what types of injuries occur—is essential.

Although individual epidemiological studies exist, the literature lacked a comprehensive meta-analysis quantifying injury incidence rates (IR) in basketball by injury mechanism (contact, non-contact, surface impact, etc.), body location (ankle, knee, head, etc.), and injury type (sprain, strain, concussion, fracture, etc.), while also comparing differences by sex, level of play, and competition vs practice exposure.

This study provides the largest and most up-to-date synthesis to date on how, where, and why basketball injuries occur, based on over 50,000 injuries recorded across more than 13 million exposures.

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

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🎯 Research Objective and Scope

The study aimed to systematically review and meta-analyze published basketball injury epidemiology data to:

1. Quantify injury incidence rates (IR) by mechanism
   (e.g., player contact, ball contact, non-contact, overuse, equipment, etc.)

2. Measure injury incidence by body location
   (e.g., ankle, knee, head/face, shoulder, etc.)

3. Classify injury incidence by type
   (e.g., sprains, strains, fractures, concussions, contusions)

4. Compare differences based on:

   • Player sex (male vs female)

   • Level of play (high school vs college vs professional)

   • Exposure setting (competition vs practice)

The goal was to identify the strongest patterns and risk factors that should shape future basketball injury prevention and performance support programs.

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⚙️ Methodology

Study Design

A systematic review and meta-analysis following PRISMA guidelines.
Databases searched through October 2024 included:

• PubMed

• Google Scholar

• MEDLINE

Inclusion Criteria

Studies were included if they:

• Reported injuries in organized basketball practices or games

• Included players aged ≥15 and classified Tier 3–5 (high school, collegiate, or professional)

• Reported exposures in athlete-exposures (AEs) or exposure hours

• Provided data on injury mechanism, location, or type

Dataset Overview

• 22 studies included

• 50,585 injuries

• 13,309,133 athlete exposures

• 375,620 exposure hours

Injury Categories Examined

Mechanism:

• Player contact

• Non-contact

• Ball contact

• Surface contact

• Out-of-bounds contact

• Equipment contact

• Overuse

• Unspecified

Body Regions:

• Head/face/neck

• Upper extremity

• Trunk

• Hip/groin/thigh

• Knee

• Lower leg

• Ankle

• Foot

Injury Types:

• Ligament sprain

• Muscle/tendon strain

• Concussion

• Contusion

• Fracture

• Dislocation/subluxation

• Abrasion/laceration

Meta-analytic comparisons were conducted for:

• Sex

• Level of play (HS vs college)

• Competition vs practice

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📊 Key Findings

1️⃣ Mechanism: How Basketball Injuries Occur

• Player contact was the No.1 injury cause, accounting for:

  • 42.9% of all injuries

  • IR = 0.156 per 100 AEs

• Non-contact mechanisms were second:

  • 25.0% of injuries

  • IR = 0.093 per 100 AEs

Competition significantly increased injury risk across nearly every mechanism:

Female athletes had significantly higher rates of ball contact injuries than males.

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2️⃣ Body Location: Where Players Get Hurt

Lower-body injuries dominated:

• 61.7% of all basketball injuries occurred in the lower extremity

  • Ankle: 25.8% (IR = 0.075 per 100 AEs)

  • Knee: 15.5% (IR = 0.046 per 100 AEs)

Competition increased IR for nearly all body locations:

• Knee: 2.8× higher

• Ankle: 2.2× higher

• Head/face: 3.2× higher

Female players had significantly higher IR for:

• Knee injuries

• Head and face injuries

• Lower leg injuries

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3️⃣ Injury Type: What Injuries Occur

The most common injury types were:

Competition increased injury incidence across most injury types:

• Concussion: 3.5× higher in competition

• Ligament sprain: 2.5× higher

• Dislocation/subluxation: 2.8× higher

Female players had significantly higher rates of:

• Knee sprains

• Dislocations/subluxations

• Concussions

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⚠️ Limitations

• Heterogeneity was high due to differences in:

  • Injury definitions

  • Reporting systems

  • Exposure measurements (AEs vs hours)

• Limited reporting of non-time-loss injuries, meaning minor injuries may be underrepresented.

• Most data derived from high school and college settings, with fewer studies on professional players.

• Inconsistent injury definitions (e.g., how “overuse injury” was classified).

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🧠 Implications for Practice

1️⃣ Basketball Needs Level-Specific Injury Prevention

Collegiate players consistently showed higher injury rates than high school athletes due to:

• Higher workloads

• More physical contacts

• Greater match intensity

2️⃣ Lower Extremity Protection Is Priority

Given that ankle and knee injuries make up 41% of all injuries, teams should prioritize:

• Neuromuscular and balance training

• Screening for high-risk athletes

• Appropriate bracing/taping (with individualized assessment)

3️⃣ Women’s Basketball Requires Targeted Support

Female athletes suffered higher rates of:

• Knee injuries

• Ball contact injuries

• Concussions

Prevention programs should address:

• Neck strength

• Neuromuscular control

• Landing mechanics

• Structural anatomical risk factors

4️⃣ Competition Is the High-Risk Environment

Teams should increase:

• Injury surveillance

• Medical coverage

• Sideline concussion checks

• Game-specific prevention protocols

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✅ Conclusion

This large-scale systematic review and meta-analysis confirms that:

• The majority of basketball injuries result from player contact and non-contact biomechanical events.

• Lower extremity injuries—especially ankle sprains and knee injuries—are the most prevalent.

• Ligament sprains, muscle strains, concussions, and contusions represent the most common injury types.

• Women sustain significantly higher rates of knee injuries, concussions, and ball-contact injuries.

• Injury risk is consistently higher in competition than practice, especially at higher playing levels.

These findings support the need for smarter load management, better injury surveillance, stronger neuromuscular prevention programs, and tailored strategies for women and collegiate athletes.

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🏒 Upside Study (2): Do NHL Injury Rates Increase During Winter Olympic Years?

Authors: Ibraheem Qureshi, Hans Drawbert, Minn H. Saing, and John T. Riehl
Published in: Orthopaedic Journal of Sports Medicine, 2025

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🔎 Introduction

The National Hockey League (NHL) is unique among major professional sports leagues in that every four years, its regular season is interrupted to allow players to participate in the Winter Olympics. These tournaments occur mid-season, requiring extensive travel, condensed competition schedules, increased physical load, and the need to rapidly return to regular league play shortly thereafter.

Prior studies in other high-performance sports—such as domestic soccer leagues affected by the mid-season 2022 FIFA World Cup or the African Cup of Nations—have found that unusual degree of congestion and match intensity can increase injury risk. For the NHL, while analyses have examined post-Olympic performance/output changes among athletes and teams, comprehensive league-wide evidence quantifying whether player injuries actually increase during Olympic seasons has been limited.

This study provides one of the most detailed analyses to date, comparing NHL injury rates and patterns during Olympic years (2002, 2006, 2010, 2014) versus standard seasons between 2000 and 2023 to determine whether Olympic participation creates measurable additional injury burden for players and teams.

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

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🎯 Research Objective and Scope

The researchers designed the study to answer three main questions:

1. Do overall injury rates increase during seasons in which NHL players participate in the Olympics?
   This includes the league as a whole, regardless of whether individual players were selected for Olympic rosters.

2. Do certain categories of injuries (knee, muscle, shoulder, etc.) increase during Olympic years?

3. Within Olympic seasons, do Olympic participants experience different injury risks compared to non-participating NHL players?

To accomplish this, the authors compared:

• Total number of injuries

• Frequency of specific body-location injuries

• Rates of multiple injuries per player

• Average games missed due to injury

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⚙️ Methodology

Dataset

The study retrospectively analyzed all NHL player injuries between 2000 and 2023, drawn from widely used public reporting databases, including:

• TSN

• CapFriendly

• CBS Sports

• SportsForecaster

These databases have been used in prior NHL injury research and record injuries severe enough to cause at least one missed official game.

Injury Seasons Compared

• Olympic Years: 2002, 2006, 2010, and 2014

• Non-Olympic Seasons: 20 remaining seasons between 2000–2023

Key Definitions

• Injury: Any issue causing a player to miss at least one official NHL game.

• Multiple Injuries: A player sustaining a second or subsequent injury in the same season.

Non-medical absences (suspensions, personal leave, tactical decisions, etc.) were excluded.

Data Collected

• Total injuries per season

• Injury distribution by position

• Location/type of injury (e.g. knee, muscle, eye, ankle, etc.)

• Mean games missed due to injury

Statistical Analysis

• Group comparisons using t-tests and chi-squared tests

• Logistic regression models adjusting for player position to determine whether Olympic seasons increased the odds of certain injury types

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📊 Key Findings

1️⃣ Overall Injury Rates

No increase in total injuries during Olympic years

Difference was statistically non-significant (P = .94).

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2️⃣ Injury Pattern Changes During Olympic Years

Although total injuries remained stable, several injury categories increased during Olympic seasons, even after adjusting for position:

• Knee injuries

  • Odds Ratio (OR): 1.71

  • P < .001

• Muscle injuries

  • OR: 1.65

  • P < .001

• Foot & ankle injuries

  • OR: 1.49

  • P < .001

• Arm & shoulder injuries

  • OR: 1.25

  • P = .001

• Eye injuries

  • OR: 1.49

  • P = .029

In addition, multiple injuries per player were significantly more likely in Olympic years (OR = 1.12, P = .002).

Notably:

• Defensemen and goaltenders showed a higher proportion of injuries during Olympic seasons compared to standard years.

• Average games missed per injury slightly decreased during Olympic seasons (7.44 vs 8.23), possibly suggesting quicker returns to play despite the higher frequency of certain injury types.

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3️⃣ Olympic Players vs Non-Olympic Players (Within Olympic Seasons)

When comparing only players within Olympic years:

• No significant difference in overall injury rates:

  GroupMean injuries per playerOlympic players1.194Non-Olympic NHL players1.486

  (P = .18)

• Olympic participants showed:

  • Higher odds of multiple injuries (OR = 1.23, P = .018)

  • Higher rates of chest/torso injury

  • Lower odds of foot/ankle and arm/shoulder injuries

• Olympic athletes specifically showed more knee and muscle injuries in Olympic seasons compared to non-Olympic seasons (P = .005 and P = .001).

This mirrors trends observed in soccer World Cups and other mid-season international events, where high-load competitions contribute to soft-tissue injury spikes once athletes return to domestic schedule congestion.

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⚠️ Limitations

The authors note several methodological constraints:

• Injury reporting depended solely on public databases, meaning:

  • Minor injuries not causing missed games were likely omitted.

  • Some injury details may lack medical precision.

• No granular match-level workload data (minutes played, shift counts, travel exposure, direct mechanisms).

• Olympic participation archives missing 1998, meaning one year of eligibility was not captured.

• Different tournament structures across sports (e.g. NHL halts season during Olympics but European soccer does not) complicate multi-sport comparisons.

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🧠 Implications for Practice

1️⃣ Player Load Management Is Key

The study suggests that Olympic seasons do not inherently increase overall league injury burden, but they do intensify soft-tissue and joint injuries, especially:

• Muscle strains

• Knee injuries

This supports prioritizing:

• Short-term load tapering before Olympic travel

• Intensified monitoring weeks immediately after return

• Increased training focus on recovery and tissue resilience

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2️⃣ Defensemen and Goaltenders Require Specific Consideration

These positions saw a higher share of injuries during Olympic seasons, possibly due to:

• Higher continuous workload demands

• Less rotational substitution compared to forwards

• Increased stress and exposure in international tournament play

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3️⃣ Return-to-League Protocols Could Reduce Multiple Injuries

Because multiple injuries per season were more common in Olympic years, organizations could benefit from:

• Structured re-entry guidelines

• Player-specific post-Olympic conditioning plans

• Reduced immediate workload ramps

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✅ Conclusion

This comprehensive 23-year database review found that:

• Olympic seasons do not increase total NHL injury rates, contradicting assumptions that mid-season international play automatically elevates league-wide injury risk.

• However, certain injury categories—including muscle strains, knee injuries, and repeat injuries—occur more frequently during Olympic seasons, particularly in players who compete in the Olympics.

• These results suggest that while the Olympic break does not harm the league at a macro level, individual players (especially Olympic participants) experience focused injury risks linked to soft-tissue overload and accelerated game cycles.

These insights can inform future NHL negotiating positions on Olympic participation as well as more proactive workload and recovery strategies for teams during Olympic years.

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Upside NFL Study: Spending on Star Players & Team Success in the NFL

🏈 Introduction

Success in the National Football League (NFL) hinges not only on recruiting elite talent but on strategically allocating financial resources within a hard, league-wide salary cap. Unlike other professional sports systems that allow massive spending disparities, every NFL franchise operates under the same financial constraints, forcing general managers to decide how best to distribute payroll: concentrate spending on a few star players, or spread investments more widely across the roster.

For years, sport media and public perception have suggested that “superstar-driven teams win championships.” Quarterbacks, elite pass rushers, and marquee wide receivers regularly command record-breaking contracts, reinforcing the belief that paying top talent is the most reliable pathway to on-field dominance.

However, comparatively little empirical work has tested whether this belief is true at the league level—or whether certain spending strategies are more efficient, more predictive of wins, or more strongly associated with playoff and Super Bowl success.

This study, covering the 2011–2020 NFL seasons, uses a rigorous efficiency-based economic model to analyze how teams deploy payroll capital across their rosters and how these decisions relate to real competitive outcomes. It evaluates four key questions:

• Do teams that spend heavily on a small number of stars actually perform better?

• Are certain positions (especially quarterback or defense) better investments than others?

• Can efficient spending—not just high spending—predict playoff advancement and championships?

• What patterns differentiate consistently successful franchises from chronic under-performers?

Using robust Data Envelopment Analysis across ten full seasons and all 32 NFL franchises, the study provides one of the clearest quantitative examinations to date of whether it is truly “all about the stars”—or whether smarter, more balanced payroll management drives winning in the modern NFL.

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

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1. Context

Understanding how teams should distribute their salary cap is a critical strategy question in the NFL. Franchises operate under a hard, universally applied cap — forcing general managers to decide whether to:

• Spend heavily on a few elites

• Spread payroll across the roster

• Prioritize certain position groups (offense, defense, QB)

This study uses 2011–2020 league data to evaluate which approaches are most payroll-efficient and how efficiency correlates with playoff and Super Bowl outcomes.

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2. Objective

The purpose of the research is to examine:

Are NFL teams more successful when they invest heavily in star players, or when they spend their payroll more evenly across the roster?

More specifically, the study evaluates whether payroll efficiency in different spending strategies correlates with:

• Wins

• Playoff qualification

• Super Bowl appearances and victories

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3. Methodology

A robust directional distance Data Envelopment Analysis (DEA) was used to evaluate the payroll efficiency of all 32 NFL teams across 10 seasons (2011–2020).

Models analyzed

Four efficiency models were created:

1. Overall payroll allocation

2. Star offensive player spending

3. Star defensive player spending

4. Starting quarterback spending

Inputs

Salary cap expenditure data, including:

• Top 5 salaries

• Salaries 6–10

• Salaries 11–30

Outputs

Team performance:

• Wins

• Points per game (PPG)

• Yards per offensive play (YOP)

• Points allowed per game (PAG)

• Yards per defensive play (YDP)

• Yards per passing attempt (YPA) for QB analysis

A Monte-Carlo robust DEA approach was used (1,000 resamples per team) to prevent single outlier teams from skewing the results.

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4. Participants

• 32 NFL Teams

• 10 full seasons of data (2011–2020)

Total dataset = 320 team-seasons

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5. Results

A. Spending Efficiently Works

Across all models:

Teams that ranked higher in payroll efficiency were significantly more likely to reach the playoffs, the Super Bowl, and win championships.

B. Defensively Efficient Spending Was the Most Predictive of Postseason Success

The strongest correlation came from:

Model 3 – spending on star defensive players

Top defensive-efficiency teams included:

• New England Patriots

• Seattle Seahawks

• Pittsburgh Steelers

For example:

• Patriots → 9 playoff appearances, 5 Super Bowls, 3 titles (2011–2020).

C. Quarterback Spending Efficiency Highlights Two Paths

The most efficient QB-spending teams generally fell into two groups:

• Long-tenure franchise stars on team-friendly deals

  • e.g., Brady, Wilson, Romo, Prescott

  • Patriots, Seahawks, Cowboys
    

    Is it all about the stars - Spe…

• Elite rookies on low-cost rookie contracts
  Teams achieved high efficiency with:

first-round rookies who were outperforming inexpensive contracts

D. Inefficient Spending Correlates with On-Field Failure

Chronic bottom-five teams included:

• New York Jets

• Jacksonville Jaguars

• Cleveland Browns

• Tampa Bay Buccaneers

• Las Vegas Raiders

These teams collectively:

Made only 4 playoff appearances in 10 years

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6. Limitations

The authors note several constraints:

• DEA multiplier formulation limits ability to set output targets

• Data only includes measurable on-field stats (not leadership, locker room effects, injuries, market size or coaching strategy)

• Certain critical variables (e.g., injuries to star players) may distort payroll efficiency scores

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7. Implications

For General Managers

The data suggests:

• Star defensive spending offers the highest competitive leverage

• Teams should seek:

  • Team-friendly deals with top QBs

  • Maximizing value during rookie contract windows

• Spending big isn’t enough — spending efficiently is what predicts wins.

For Coaches & Performance Directors

Performance impact is maximized when:

For Analysts

Payroll efficiency may serve as a:

leading performance indicator
that predicts team postseason performance better than raw spend or cap allocation alone.

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8. Recommendations

NFL teams can improve competitive positioning by:

1. Prioritizing defensive star efficiency

• Do not underinvest in elite defensive roles (CB, edge, defensive disruptors)

• This category most strongly predicts playoff advancement.

2. Exploiting QB contract windows

• Maximize championship runs when elite QBs are on rookie deals

• Minimize high cap % bloat for aging or plateauing quarterback performance

3. Avoiding sunk-cost bias

Do not force playing time or strategic prioritization because of salary — many failing teams continued overplaying expensive underperformers.

Is it all about the stars - Spe…

4. Enhancing front-office decision frameworks

Teams should incorporate:

• DEA-style efficiency benchmarking

• Salary cap resource allocation modeling

• Injury and absence adjustments (significant performance impacts)

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Conclusion

Across a decade of data:

Payroll efficiency—not total spending—was the strongest predictor of playoff qualification and Super Bowl success.

And:

Investment in star defensive talent produced the highest postseason returns across all models.

Teams that:

• Managed the cap efficiently

• Avoided overpaying singular stars

• Exploited rookie-contract windows

• Built strong defensive cores

were statistically more likely to reach the postseason and win championships than teams that simply spent more.

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