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What sports analytics does—and what it cannot do
Analytics is a decision process, not a single advanced statistic or a machine that supplies the right answer. A team starts with a practical question—such as whom to scout, what a player contributes, which tactic to prepare or how to manage workload—and gathers evidence that may help answer it. Measures summarize what happened or estimate what might happen; people still have to decide what those results mean for a particular player, opponent and situation.
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A useful distinction is between description and prediction. A descriptive measure records or summarizes something, such as a player’s distance covered. A predictive estimate attempts to assess a future or unobserved outcome, such as the likelihood of a shot going in. Either can be useful, but neither automatically explains why an outcome occurred. A relationship between a tactic and winning, for example, does not by itself establish that the tactic caused the win.
Uncertainty remains even when a decision is well supported. A peer-reviewed review of team-sport analytics puts it plainly: “A good decision based on a higher probability of desired outcomes can still have an outcome that is a less preferred one.” A team can make a sensible choice and lose; a weak choice can work out. The quality of the decision should be judged by the evidence and reasoning available when it was made, not solely by the result.
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Because teams often keep their methods private, public examples show what analytics can do, not exactly how every club uses it or how much influence a model has on a particular decision. The cross-sport review notes that many club applications are not publicly available. It is therefore more accurate to speak of documented uses and common decision questions than to claim that all teams follow one formula.
How the analytics process works
- Define the decision. A question such as “Which player best fills this role?” is more useful than “What can we learn from the data?” The decision determines which evidence matters.
- Assemble relevant evidence. Teams may combine box scores and event records with video-coded actions, tracking, scouting judgments, and training or sensor data. Different sources capture different parts of performance.
- Choose and interpret measures. Analysts select measures suited to the question and consider whether they describe an outcome, estimate one, or serve as a proxy for something harder to observe.
- Put the signal in context. Role, teammates, opponent, game state, health, availability and cost can change what a number means. Video and staff observations can help explain what a summary measure leaves out.
- Make and review the choice. Coaches, executives or performance staff use the evidence alongside their expertise. The eventual result can inform future evaluation, but a single win or loss does not prove that the process was sound or unsound.
The practical test at each stage is whether the information helps with the decision at hand. More data is not automatically better: a measure can be noisy, incomplete or poorly matched to the question, and a precise-looking estimate can still rest on limited evidence.
How teams use analytics to evaluate players
Player evaluation is not just a search for the largest individual statistic. Teams want to estimate how a player contributes to winning in a particular role, how likely they are to be available, and what acquiring or retaining them costs. Those considerations can point in different directions: a player’s performance may be valuable, but availability or salary may affect whether that contribution makes sense for a roster.
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A documented historical baseball example comes from the Oakland Athletics. The cross-sport review describes the club using on-base percentage and slugging percentage to identify players it considered undervalued relative to their contributions. These measures provided one way to look beyond familiar conventional evaluations; they were not a complete formula for player value, nor evidence that every team or sport now uses the same approach.
A sound comparison asks what the statistic captures and what it misses. A measure may reflect a player’s output without accounting for role, teammates, opportunities or the opponent. In sports where players jointly create opportunities, attributing the result only to the last action can obscure the contributions that made it possible. Evaluation is stronger when the number is interpreted alongside video, scouting and an understanding of the player’s responsibilities.
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Analytics can also help teams narrow a scouting search or compare candidates, but a ranking is not the same as a decision. Staff still need to judge whether a player fits the roster and intended role, whether the observed evidence is representative, and how availability and cost affect the choice.
What player tracking measures
Traditional event records and box scores focus on recorded actions and outcomes. Tracking adds information about where players and the ball were and how they moved, including movement away from the ball that may not appear in a conventional event log. This can help staff study spacing, speed, acceleration, defensive positioning or the sequence leading to an outcome.
Baseball: Statcast and camera tracking
MLB’s Statcast glossary describes measures covering pitching, hitting, running and fielding. The glossary says each MLB club ballpark has 12 Hawk-Eye cameras: five high-frame-rate cameras focused on bat and pitch tracking, and seven tracking players and batted balls. Those are the system details stated in the glossary, not a claim about every tracking system used in every sport.
In baseball, tracking can add detail to a recorded play: a pitch’s movement, a batted ball’s trajectory or a runner’s speed, for example. These observations give teams more context than a basic record of the play’s result, but a measurement still needs interpretation. A player’s tracked movement does not by itself explain the tactical reason for it or determine what should happen next.
Soccer: location and movement data
For the 2020 season, Major League Soccer announced an arrangement with Second Spectrum to capture the location of every player, referee and the ball 25 times per second. The league 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 describes the arrangement announced for that season; it should not be read as confirmation of MLS’s current provider.
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MLS senior vice president of media Chris Schlosser called the partnership “a new standard for innovation in player-tracking in soccer.” That was a statement in the league’s announcement, rather than an independent evaluation of the system. The concrete use described was the capture of tracking data and delivery of reports and video tools to club staff.
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An NBA-hosted 2025–26 public analytics effort describes examples drawing on player-pose tracking. The league’s account includes a shot-difficulty estimate informed by the shooter’s position and defender proximity, defensive box-score attribution, a contextualized “gravity” metric, and a tool for finding similar movement patterns in plays. These examples show how tracking can support richer descriptions than a basic event outcome alone.
They should be understood as league-described capabilities, not proof that every team uses every measure internally or that every announced feature is available in every setting. The underlying question remains contextual: a defender’s proximity, for instance, may help characterize a shot, but it does not explain every factor that shaped the possession.
How teams use data to choose tactics
Tracking and event data can help teams prepare for opponents and assess how a tactic unfolds. Coaches may use reports and video to examine where players move, how a defense responds, or which sequences resemble situations they want to study. The value comes from connecting a measured pattern to a tactical question—not from treating a statistical association as proof that a particular strategy causes success.
Sport structure affects what can be measured well. The cross-sport review notes that continuously flowing games, with important interactions away from the ball, can be harder to represent than stop-start sports. When scoring events are limited, teams also have fewer direct observations of the outcomes they ultimately care about. That makes context, video and careful interpretation especially important: a small number of goals or points may not adequately represent the quality of all the actions that created or prevented chances.
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A useful way to assess a tactical measure is to ask whether it captures the relevant interaction. If the question concerns spacing or a defensive shape, a count of the final action may miss the movement that created it. Tracking can help fill that gap, but a model still simplifies a complex sequence involving multiple players and changing game conditions.
How analytics informs decisions during a game
Analytics is not limited to advance scouting or postgame review. It can help staff think through an in-game choice, where time is limited and the likely value of an action depends on the precise situation. A 2026 example from MLB’s Automated Ball-Strike (ABS) metrics illustrates the difference between whether an action is allowed and whether it is strategically sensible.
MLB’s ABS metrics documentation distinguishes a “challenge opportunity”—an eligible call—from a “reasonable pitch,” which adds strategic context. That context may include whether the call was actually wrong, how valuable a close pitch was, or whether the call was likely to be challenged. Eligibility alone therefore does not answer whether a challenge is worthwhile. This is a specific example under MLB’s rules and measurement framework, not a general rule for other sports.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How teams monitor workload and recovery
Performance staff may use workload data to inform training plans, recovery discussions and return-to-play planning. A 2026 report on the Miami Marlins describes GPS units used to collect sprint-speed and distance information. Staff use the information to discuss workload and recovery, plan between series and support return-to-play conversations.
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The report also emphasizes that measured output is only one part of the picture. Marlins director of strength and conditioning Jeffrey Taylor called GPS “just a piece of the puzzle” alongside medical, performance, nutrition, mental-health and wellness work. Staff also need to listen to what a player says about their body. The report supports describing monitoring and planning, not claiming that a GPS measure diagnoses an injury or reliably predicts one for an individual.
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That distinction matters because an observed change in distance or speed may prompt a question, but it does not independently establish its cause. Health decisions require medical and player context; a performance measure should inform those conversations rather than replace them.
Why analytics methods differ across sports
The same analytic approach will not transfer cleanly from one sport to another. Sports differ in pace, rules, the importance of off-ball movement, the frequency of scoring and the amount of relevant data available. In a flowing game, many interactions may happen between recorded events; in a stop-start format, discrete actions can be easier to isolate. Neither structure eliminates uncertainty, but it changes what analysts can observe and how much context a measure must recover.
The review’s historical examples also illustrate how analytics staffing grew in MLB: it reports 44 club “quants” in 2009, 75 in 2012 and 156 in 2016, drawing on Lindberg and Arthur’s study of club materials. These are historical counts reported by the review, not current staffing figures. They show increased attention in that period, not the size or organization of analytics departments today.
For scale, the same review lists regular-season formats of 162 games for MLB, 82 for the NBA and NHL, 38 for the English Premier League, and 16 for the NFL. Those are the formats stated in the review’s discussion of data volume, not a verified statement of current schedules. Game counts help explain why available observations differ, but the number of games alone does not determine data quality: the relevance and consistency of each observation matter too.
How to judge whether an analytic claim is useful
When a team or commentator presents a metric, readers can evaluate it by asking a few questions before treating it as a verdict:
- What decision is it meant to inform? A statistic useful for scouting may not answer an in-game tactical question.
- What is actually measured? Determine whether the number records an event, describes a pattern, estimates a probability or acts as a proxy for a broader idea.
- Does it include off-ball context? An event-only record may miss movement and interaction that helped create the outcome.
- How much relevant evidence is there, and how noisy might it be? A rare outcome or small set of observations can make comparisons uncertain.
- What context could change its meaning? Role, teammates, opponent, game state and health may affect both performance and opportunity.
- Are availability and cost part of the decision? Player contribution is only one consideration in building a roster.
- What would count as evidence that the decision process worked? A single outcome is not enough to establish that the model or tactic caused it.
These checks apply whether the number comes from a public league tool or a club’s private analysis. A model can make evidence easier to compare, but the decision still depends on whether the evidence fits the question and whether staff account for what it leaves out.
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