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Sports teams use analytics to make better-informed decisions about whom to recruit, how to build a roster, what tactics to prepare, when to adjust during a game, and how to manage training and recovery. They combine statistics with video, scouting, player-tracking and other context; analytics estimate what may happen, but they cannot guarantee an outcome or replace the judgment of coaches, scouts, medical staff and players.
What sports analytics is—and what it is not
Sports analytics is a decision process, not a single statistic or a model that tells a coach what to do. A team begins with a question, gathers evidence relevant to it, interprets the evidence in context and decides whether it should change a choice. That evidence can include game statistics, tracking data, video-coded actions, scouting observations and, where relevant, workload measurements.
The question matters. “Who scored the most?” is not the same as “Which available player is likely to improve this roster in this role, at this cost?” A statistic can describe what happened without explaining why it happened; a model may estimate what is likely to happen without establishing that a particular tactic caused it. Teams therefore need to distinguish a direct measure from a proxy, and a probability from a promise.
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A peer-reviewed cross-sport review makes the central uncertainty explicit: “A good decision based on a higher probability of desired outcomes can still have an outcome that is a less preferred one.” A sound decision can end badly because sport is uncertain; one lucky success does not by itself prove the decision was sound. The review of playing-side analytics in team sports describes analytics as a way to support more informed choices, not certainty.
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How teams turn data into a decision
- Define the decision. The staff specifies what it needs to choose: a prospect to scout, a roster move, a matchup, a tactical adjustment, or a workload plan.
- Select useful evidence. Analysts assemble relevant records such as event logs, box scores, video, tracking, scouting assessments or training information. Different sources answer different questions; a box score alone, for example, may not show a player’s movement away from the ball.
- Check the measure and its limits. Staff ask whether a number directly measures the target or stands in for it, how much relevant data exists, and what could distort the comparison.
- Interpret the signal in context. Role, teammates, opponents, game state, health and cost can change what the same observed performance means.
- Make and review the choice. A coach or other responsible staff member weighs the evidence with experience and relevant expertise. Teams can later compare the decision with what happened, while remembering that the result alone cannot reveal whether the original reasoning was good.
Much of the detailed analysis clubs use is proprietary. Public examples show possible applications, not a complete account of each team’s methods or how much weight it gives a particular metric.
How teams use analytics to evaluate players and build rosters
Player evaluation is stronger when it asks how a player contributes to winning in a specific role, whether the player is likely to be available, and what the player costs. No single headline number answers all three questions. A high individual total, for instance, does not automatically show how a player fits with teammates or whether the same output is likely in a different role.
A historical Oakland Athletics example in the cross-sport review illustrates how a measure can help identify value. The club used on-base percentage and slugging percentage to find players considered undervalued relative to their contribution. The example is not a claim that every current club uses those statistics as its standard, or that those measures alone determine a roster. It shows how a team can look beyond a familiar evaluation and compare likely contribution with salary cost and availability. The review discusses the Athletics example and broader roster considerations.
In practice, a useful player comparison begins by making the question specific: contribution to which team need, in which role, against what alternatives? Staff can then combine recorded performance with scouting and video, and consider whether the player is a fit for the existing group. Opportunity and context matter: the same action can have different value depending on teammates, opponent, position and game situation. Analytics can help organize and test those comparisons; it cannot erase uncertainty about future performance.
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What player tracking measures
Traditional event records tell teams that a recorded event occurred. Tracking can add where players and the ball were and how they moved before, during and after it. That location and movement context can help analysts examine off-ball actions that a conventional event record may not capture, as well as speed, spacing and the sequence leading to an outcome.
Tracking systems and derived measures vary by sport and provider. MLB’s Statcast glossary describes measures across pitching, hitting, running and fielding. It says each MLB club ballpark uses 12 Hawk-Eye cameras: five high-frame-rate cameras focus on bat and pitch tracking, while seven track players and batted balls. Those are the glossary’s described system details, not a claim that every tracked measure answers every evaluation question. MLB’s Statcast glossary outlines the categories and camera setup.
For soccer, Major League Soccer announced for the 2020 season that Second Spectrum would optically track every player, referee and the ball at 25 location captures per second. The announcement said the system would derive measures including speed, acceleration, deceleration, passing probability and expected goals, and provide customized reports and video tools for club technical staff. This documents the arrangement announced for that season; it should not be read as confirmation of MLS’s current provider arrangement. MLS’s 2020 announcement describes the system and its intended staff-facing uses.
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How teams use data to choose tactics and prepare for opponents
Coaches and analysts can use event and tracking data, alongside video, to examine how a team creates or concedes opportunities. Tracking can expose spacing and movement between recorded events; video can help staff understand the sequence and tactical context. The aim is to identify patterns worth preparing for—not to assume that a pattern observed in one situation will produce the same result in another.
The NBA-hosted account of a 2025–26 public analytics effort describes examples based on player-pose tracking: estimating shot difficulty using a shooter’s position and defender proximity, attributing defensive contributions in box-score form, contextualizing a player’s “gravity,” and finding similar sequences of play by grouping movement patterns. These examples illustrate how tracking can support analysis of defensive pressure, off-ball influence and recurring actions. They are league-described capabilities, not evidence that every team uses every metric internally or that each feature is available in every setting. The NBA account describes the public analytics effort and its examples.
Methods do not transfer cleanly from one sport to another. The cross-sport review notes that continuously flowing games with important off-ball interactions are harder to represent than stop-start sports. Where scoring events are limited, there may also be relatively few direct observations of the outcomes a team wants to predict. This affects how confidently staff can interpret a metric and how much context they need around it. The review explains these cross-sport challenges.
How analytics can inform decisions during a game
Analytics can support an in-game choice as well as advance preparation. A concrete, sport-specific example comes from MLB’s 2026 Automated Ball-Strike (ABS) metrics documentation, which distinguishes an eligible call from a strategically sensible challenge. A “challenge opportunity” is an eligible call. A “reasonable pitch” adds strategic context: whether the call was wrong, how valuable a close pitch was, or whether a challenge was likely.
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The distinction matters because eligibility alone does not tell a team whether using a challenge is worthwhile. The value of acting depends on what is at stake and what the team believes about the call. This is an MLB rules and measurement example, not a universal formula for in-game decisions in other sports. MLB’s ABS metrics documentation defines the terms.
How teams use workload data—and what it cannot establish
Some teams collect training or workload information to help staff discuss recovery, plan activity and support a player’s return to play. A 2026 report on the Miami Marlins describes GPS units used to measure sprint speed and distance. Staff use that information in workload and recovery discussions and to plan between series and discuss return to play. These measurements can describe activity; they are not, by themselves, a diagnosis or a guaranteed forecast of an individual injury.
Marlins director of strength and conditioning Jeffrey Taylor described GPS as “just a piece of the puzzle,” alongside medical, performance, nutrition, mental health and wellness work. The report also underscores the importance of listening to what a player says about their body. A measured output should therefore be considered alongside player feedback and other assessments, rather than treated as a standalone instruction or injury prediction. The Marlins report describes its GPS use and staff’s qualifications.
How to judge whether a statistic is useful
Before relying on a metric, ask what decision it is meant to improve and whether the data actually captures the relevant performance. A measure can be descriptive, predictive or a proxy; those are different jobs. A descriptive figure summarizes observed activity. A predictive estimate concerns likely future outcomes. A proxy stands in for something harder to measure. None should be mistaken for a complete account of a player or a tactic.
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- Decision: Is the team choosing a player, tactic, in-game action or workload plan?
- Coverage: Does the signal capture only recorded events, or also movement and off-ball context?
- Evidence: How many relevant observations exist, and how noisy or situation-dependent might they be?
- Meaning: Is the number directly measured, a prediction, or an indirect indicator?
- Context: Do player role, teammates, opponents, health and game state change its interpretation?
- Practical fit: How do likely contribution, availability and cost compare with the alternatives?
These checks help prevent a common error: assigning all credit or blame to the last visible action when teammates’ interactions helped create the opportunity. The cross-sport review emphasizes that collaborative play complicates individual attribution, and that data availability and game structure influence what can be inferred. A figure may be useful for one decision yet incomplete for another.
Why analytics does not replace coaches, scouts or players
Numbers can reveal patterns that are difficult to notice consistently, test assumptions and make comparisons more explicit. They do not decide which goals matter, resolve every conflict between evidence and experience, or account perfectly for all the circumstances around a performance. Coaches interpret tactics and game state; scouts assess qualities and fit; medical and performance staff contribute expertise about health and readiness; players provide knowledge of their own experience. Analytics is most useful when it makes that decision-making better informed rather than pretending the uncertainty has disappeared.
Interest in quantitative staff has grown historically, but the available figures should not be mistaken for current headcounts. The cross-sport review reports 44 MLB club “quants” in 2009, 75 in 2012 and 156 in 2016, drawing on Lindberg and Arthur’s study of club materials. Those are review-reported historical counts, not present-day staffing totals. The review also gives regular-season game counts in its discussion of data volume—MLB 162, NBA and NHL 82, English Premier League 38, and NFL 16—as the league formats it describes. They are not verified current schedules. The broader point is that sports differ in how much relevant game data they generate and in how directly recorded outcomes reflect the performance teams care about. The review discusses staffing history, game formats and data-volume challenges.
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