Learning

Think of per-100 possession stats like converting currencies to a common denomination. Raw counting stats are distorted by two things: team pace (faster teams create more possessions per game) and minutes played (a starter logging 35 minutes simply has more opportunities than a bench player logging 22). Per-100 possession rates strip away both of these distortions.

The key is that per-100 stats normalize by the player's own possessions played, not the team's total. If a player was on the floor for 75 possessions and recorded 2 steals, their rate is 2.0 / 75 × 100 = 2.67 steals per 100 possessions.

This matters because per-game stats hide both pace and playing time. Consider two players who each average 1.5 steals per game. Player A plays 34 minutes on a 102-pace team — roughly 72 possessions. Player B plays 26 minutes on a 96-pace team — roughly 52 possessions. Player A's rate is 1.5 / 72 * 100 = 2.08 per 100. Player B's rate is 1.5 / 52 * 100 = 2.88 per 100. Same per-game number, but Player B is a far more disruptive defender per opportunity.

This logic extends across the entire box score. Turnover rate (TOV%) measures the percentage of a player's possessions that end in turnovers, not raw turnover counts. Offensive rebound percentage (ORB%) measures the share of available offensive rebounds a player grabs, not the raw total. Usage rate measures the share of team possessions a player "uses" (via a shot, turnover, or free throw trip) while on the court. All of these are possession-based rates, and all of them are more useful than the raw counts.

The practical takeaway is simple: whenever you see a per-game counting stat, ask yourself whether pace and minutes are inflating or deflating the number. Per-100 possession stats are not perfect — they do not account for quality of competition or lineup context — but they remove the two largest sources of noise in raw box score data.

League-average True Shooting percentage has risen steadily over the past two decades, from roughly 52% in the early 2000s to around 58% in recent seasons. This shift is driven by the three-point revolution, rule changes favoring offense, and improved shot selection. The consequence: a raw TS% number means something entirely different depending on when it was recorded.

Relative True Shooting (rTS%) solves this by subtracting the league-average TS% for that season from the player's TS%. The result tells you how far above or below average the player was in their specific context. A player shooting 56% TS in the 2004-05 season, when league average was ~52%, had an rTS% of +4.0 — an elite scorer. That same 56% TS in the 2024-25 season, when league average is ~58%, yields an rTS% of -2.0 — below average.

This distinction matters enormously for historical comparisons. Allen Iverson posted a 54.3% TS in 2005-06 — slightly above the ~53.5% league average that season, giving him an rTS% of +0.8. By modern standards, 54.3% would yield an rTS% of roughly -3.7, making him look like an inefficient scorer. But in his era, he was a tick above average. Meanwhile, a current player at 58% TS might appear more efficient than Iverson ever was, but they are simply average for their era. rTS% captures this context and makes cross-era comparisons meaningful.

The DataBallr stats table and PvP comparison views both show rTS%, and it is one of the most informative single numbers for evaluating scoring efficiency. When you see a player's rTS%, you are seeing their efficiency relative to the competition they actually faced, not an absolute number divorced from context.

The table below tracks three of the most important league-wide statistics from 2001 to 2026: True Shooting percentage (TS%), turnovers per 100 possessions (TOV/100), and offensive rebound percentage (ORB%). Together, these three numbers map directly to the Three Factors framework and reveal how fundamentally the game has changed.

Shooting efficiency has risen dramatically. League-average TS% climbed from 51.4% in 2001 to a peak of 58.1% in 2023 — a 6.7-percentage-point gain. This rise was driven by the three-point revolution (teams shooting more threes at improving rates), the extinction of the long midrange jumper, and rule changes that opened up offensive spacing. The 2012 season (52.4%) stands out as an outlier — that was the lockout-shortened season where compressed schedules and limited practice time depressed shooting across the league.

Turnover rates have steadily declined. Teams committed 16.3 turnovers per 100 possessions in 2001 but just 13.7 per 100 by 2024 — a reduction of roughly 16%. Modern offenses emphasize ball security, spacing reduces the chaotic drives that produce turnovers, and offensive schemes have become more structured. The recent uptick to ~14.6 in 2026 may reflect rule enforcement changes or shifting styles of play.

Offensive rebounding tells the most complex story. ORB% dropped from 31.1% in 2001 to a low of 25.7% in 2021 — a 17% decline — as teams prioritized transition defense over crashing the glass. The logic was straightforward: in a three-point-heavy league, getting back in transition was more valuable than chasing long rebounds. But since 2021, ORB% has rebounded sharply to 30.2% in 2026, nearly returning to early-2000s levels. This reversal suggests teams are finding new value in offensive rebounding or adjusting their transition-vs-crash calculus.

These three trends illustrate why era-adjusted stats like rTS% are essential. A player's raw TS% means something entirely different in a 51% league (2001) versus a 58% league (2023). The same applies to TOV% and ORB% — what counts as "good" ball security or rebounding effort has shifted as the league-wide baselines moved.

Dean Oliver's Four Factors of basketball success — effective field goal percentage (eFG%), turnover rate (TOV%), offensive rebound percentage (ORB%), and free throw rate (FTA/FGA) — account for roughly 90-95% of the variance in offensive and defensive efficiency. Everything else in basketball is downstream of these four things. If your team shoots well, takes care of the ball, grabs offensive boards, and gets to the line, you win most of the time.

DataBallr collapses the four factors into three by combining eFG% and free throw rate into a single "shooting efficiency" factor represented by True Shooting percentage. TS% already captures both field goal efficiency and free throw value in a single number, so separating them would be redundant. The result is a cleaner three-factor framework: Shooting (TS%), Turnovers (TOV%), and Rebounding (ORB%/DRB%).

This three-factor model maps directly to the Six-Factor RAPM decomposition used on the ShotQuality page. Six-Factor RAPM breaks a player's total impact into six components: offensive and defensive contributions to each of the three factors. oTS measures a player's impact on team shooting efficiency. oTOV measures their impact on team turnover rate. oREB measures their impact on team offensive rebounding. The defensive counterparts (dTS, dTOV, dREB) measure how much the player helps or hurts the team on the other end.

The practical value of the three-factor framework is prioritization. When evaluating a lineup or a player, start with the three factors. If a lineup has a great net rating but terrible ORB%, you know the rebounding glass is a vulnerability even if the overall numbers look good. If a player has a strong overall RAPM but it is driven entirely by oTOV (they do not turn it over), you have a more specific picture of what they actually contribute.

The core math of shot selection is points per shot (PPS). A two-point field goal made at 50% yields 2 × 0.50 = 1.00 PPS. A three-point field goal made at 33.3% also yields 3 × 0.333 = 1.00 PPS. So a 50% two-pointer and a 33.3% three-pointer produce identical expected value on the make-or-miss level. Since the league-average three-point percentage is roughly 36%, the average three is worth about 1.08 PPS. That means a two-point shot needs to be made at 54% to match the average three — a high bar.

But make-or-miss PPS is not the full picture. When a shot misses, the offense has a chance to grab the offensive rebound and score again. Offensive rebound rates vary dramatically by shot zone: rim misses are recovered at ~39%, short midrange at ~32%, above-the-break threes at ~27%, corner threes at ~28%, and long midrange at just ~24% — the lowest of any zone. This means the true expected value of a rim attempt is higher than raw PPS suggests (misses frequently lead to second chances), while long midrange misses are the least likely to generate additional scoring opportunities.

Long two-point shots — those from 16 to 23 feet, the classic midrange jumper — are where this combined math is most devastating. Most players shoot approximately 40% from that range, yielding just 0.80 PPS. That is 26% less efficient than an average three-pointer on a make-or-miss basis, and long midrange misses produce the fewest offensive rebounds of any zone. When the analytics revolution reached NBA front offices in the early 2010s, this was one of the clearest signals: the long two is a bad shot for most players.

But "most" is not "all." The short midrange (10-16 feet) survived because skilled shot creators shoot 48-52% or higher from there, especially off the dribble — and when they miss, short midrange misses are offensive rebounded at a meaningfully higher rate (~32%) than long twos (~24%). Certain elite midrange shooters — DeMar DeRozan, Kevin Durant, Chris Paul — sustain long midrange percentages well above 50%, clearing the efficiency threshold outright. The analytical case killed the lazy midrange, not the midrange itself. A Durant pull-up 18-footer at 52% is 1.04 PPS, competitive with an average three.

There is also a creation dimension that raw PPS misses. A player who can credibly score from the midrange forces the defense to extend, which opens driving lanes and creates kick-out threes for teammates. Shot quality and shot value are not the same thing — a shot that generates gravity has indirect value beyond its own conversion rate. The complete analytical picture accounts for direct efficiency, offensive rebounding probability on misses, and the ecosystem effects of a player's shot diet.

In the playoffs, where defensive intensity rises and three-point percentages typically drop, the short midrange becomes even more valuable. Half-court offense in tight games often runs through the midrange, because these shots are harder for defenses to take away than threes and less reliant on getting all the way to the rim against set defenses. The midrange is a pressure release valve when the easy shots dry up.

The most fundamental question in player evaluation is: "How does the team perform with this player on the court versus without them?" On/off differentials attempt to answer this directly. Take a team's offensive and defensive rating when a player is on the court, subtract the ratings when the player sits, and the difference is the player's on/off split. A player whose team is +5 per 100 possessions better when they play seems valuable.

The fatal flaw is that on/off splits do not control for who else is on the court. When a star sits, their minutes are typically filled by the weakest bench players. When a bench player sits, the starters take over. So a star's "off" numbers are dragged down by bad teammates, inflating the differential — and a bench player's "off" numbers benefit from the starters, deflating or even inverting the signal.

This creates well-documented absurdities. A mediocre player on a team with a deep bench rotation can have terrible on/off numbers because the team is good even without them. A bench player who only appears in garbage-time blowouts alongside starters can post elite on/off numbers. In the 2016-17 season, several role players on the Warriors had better on/off numbers than Kevin Durant and Stephen Curry because those role players shared all their minutes with stars while the stars' "off" minutes included each other's rest.

On/off data is still useful — it reveals how the team performs in different configurations, and it is the raw material from which better metrics are built. But it is not an individual player evaluation tool. It measures lineup quality differences, not individual contribution. Treating on/off differentials as player grades is one of the most common analytical mistakes in basketball discourse.

RAPM addresses the fundamental flaw of on/off differentials: confounding teammates. The insight is that every stint — the stretch of play between substitutions — has exactly 10 players on the court simultaneously. If you collect enough stints, you can use regression to disentangle each player's individual contribution from their teammates' and opponents' contributions.

The setup is a massive regression. Each stint becomes a row of data. For every player in the league, there is a column with +1 if the player is on offense, -1 if on defense, and 0 if not on the court during that stint. The dependent variable is the point differential per 100 possessions for that stint. The regression simultaneously estimates each player's contribution while controlling for all nine other players on the court. This is a dramatic improvement over raw on/off splits.

Without regularization, this regression would overfit badly. Many players share the court so frequently that their individual effects are nearly impossible to separate (multicollinearity). Players who only played 200 minutes might get extreme ratings — plus or minus 10 per 100 possessions — based on tiny samples. Ridge regression fixes this by adding a penalty that shrinks extreme estimates toward zero. The principle is sound: extraordinary claims require extraordinary evidence. A player rated +8 on 200 minutes of data should be pulled toward zero because we simply do not have enough information to trust that estimate.

The result is that RAPM values are typically smaller in magnitude than raw on/off numbers. A star player might have a +6 raw on/off differential but a +3 RAPM. That compression is not a bug — it is the regularization removing noise and teammate confounds. The remaining signal is cleaner and more predictive of future performance.

RAPM is the backbone of nearly every modern impact metric. RPM, EPM, LEBRON, DARKO, and other publicly available metrics all build on RAPM or use similar adjusted plus-minus frameworks as their foundation. Some add box score priors (using counting stats to improve estimates for low-minute players), but the core engine is always some form of regularized regression on lineup data.

Different basketball statistics require vastly different amounts of data before they become reliable. Three-point percentage needs approximately 750 attempts — roughly two to three full seasons for most rotation players — before the signal outweighs the noise. Free throw percentage stabilizes faster, at around 300 attempts. Steal rate needs 2,000 or more defensive possessions. These thresholds are called stabilization points: the sample size at which a stat is roughly half signal and half noise.

Single-game plus-minus is perhaps the noisiest commonly cited stat in basketball. A player can be +20 in a game where they played well and -15 the next game while playing almost identically. The variance in single-game point differential is enormous relative to any individual player's contribution. Over a 48-minute game with 100 possessions, random variation in shooting, turnovers, and opponent quality can easily swing the score by 10+ points in either direction. Individual plus-minus absorbs all of this noise.

Season-level stats are substantially more reliable, but even full-season samples can be noisy for low-frequency events. A player who blocks 2 shots per game over 82 games has 164 blocks — a reasonable sample for per-game rate, but blocks are highly variable game-to-game. Three-point percentage at 5 attempts per game for 82 games gives 410 attempts, still short of the ~750 needed for stabilization. Multi-year samples are most reliable but introduce a different problem: players change over time. The player you are measuring over 3 seasons may not be the same player at the end as they were at the beginning.

The practical lesson is to calibrate your confidence to the sample size. Strong claims require strong evidence. A 10-game hot streak from three does not mean a player has "figured it out." A 200-minute RAPM sample does not reliably identify a player's true impact. The question is not whether data is useful at small samples — it always provides some information — but how much weight to put on it relative to priors.

Suppose a lineup plays 50 possessions together and scores at a 120 offensive rating. Is that lineup truly a 120 ORTG group? Almost certainly not. With only 50 possessions, random variation — a couple of lucky threes, an opponent turnover streak, a favorable whistle run — can easily push a 112-quality lineup to 120 or above. Regression to the mean says the true value is somewhere between the observed 120 and the league average (~112), with the exact blend depending on the sample size.

The DataBallr WOWY page implements this principle through its padding feature. When you toggle padding on, the site blends a lineup's actual performance with league-average performance based on the number of possessions played. The formula is conceptually simple: Padded Rating = (Actual Rating × Possessions + League Average × Prior Weight) / (Possessions + Prior Weight). More possessions means the actual data dominates. Fewer possessions means the league average pulls harder.

This is not arbitrary smoothing — it is mathematically principled. Padded (regressed) ratings are better predictors of future performance than raw ratings because they filter out small-sample noise. A lineup with a 130 offensive rating over 30 possessions is almost certainly not a 130 ORTG lineup, and padding correctly discounts that extreme observation. A lineup with a 115 offensive rating over 500 possessions is probably close to a true 115 ORTG group, and padding barely adjusts it.

The intuition is straightforward: extraordinary claims require extraordinary evidence. An 80% three-point shooter on 5 attempts is not an 80% three-point shooter — they are a small-sample anomaly that should be regressed heavily toward the ~36% league average. A 42% three-point shooter on 500 attempts is probably a genuinely good three-point shooter, and regression barely budges their estimate. Padding applies this same logic to lineup ratings, player stats, and any other metric built on limited observations.

Field goal percentage is the oldest and simplest shooting metric: made shots divided by attempted shots. It has two critical blind spots. First, it treats all made field goals equally — a two-point layup and a three-point bomb both count as one make out of one attempt, even though the three is worth 50% more. Second, it completely ignores free throws, which are one of the most efficient scoring methods in the game. FG% can rank a high-volume free throw merchant below a player who never gets to the line.

Effective field goal percentage (eFG%) fixes the first problem. The formula adds a 50% bonus for made three-pointers: eFG% = (FGM + 0.5 × 3PM) / FGA. This accounts for the extra point a three-pointer provides. A player who goes 4-for-10 with all four makes being threes has an eFG% of (4 + 0.5 × 4) / 10 = 60%, reflecting the 12 points scored on 10 attempts. Without the adjustment, their raw FG% would be 40%, which dramatically understates their efficiency.

True Shooting percentage (TS%) fixes both problems. It captures scoring efficiency from all three sources — two-point field goals, three-point field goals, and free throws — in a single number. The formula is TS% = Points / (2 × TSA), where TSA (True Shooting Attempts) = FGA + 0.44 × FTA. The 0.44 multiplier estimates the number of possessions consumed by free throw trips. It is not 0.5 because not all free throws use a full possession: and-one free throws, technical free throws, and flagrant free throws all come without costing a possession.

TS% is the gold standard for single-number scoring efficiency and is what DataBallr uses throughout the platform. When you see rTS% (relative True Shooting), it is TS% adjusted for league context. The gap between a player's eFG% and TS% reveals how much of their scoring comes from free throw generation — players like James Harden and Joel Embiid historically show large gaps because they get to the line at elite rates.

ShotQuality (SQ) measures the expected value of a shot based on everything known before the ball reaches the rim. It considers shot location (distance from the basket, angle), defender proximity, shot type (catch-and-shoot vs off-the-dribble), whether the shooter is in transition or half-court, and game situation factors. A wide-open corner three has a higher SQ than a contested pull-up from 22 feet, because historically the first shot goes in far more often than the second.

SQ Expected tells you about shot creation — are the player or lineup generating good looks? A team with a high SQ Expected is creating open shots, getting to the rim, and running effective offense. A team with a low SQ Expected is settling for tough shots, failing to create separation, or operating against elite defenses. This is a measure of process, not outcomes.

The gap between SQ Actual and SQ Expected reveals shot making — conversion skill beyond what the shot quality would predict. A player whose SQ Actual exceeds their SQ Expected is making shots at a rate better than the quality of their looks would suggest. They are beating the model. Conversely, a player whose SQ Actual trails their SQ Expected is missing makeable shots — perhaps due to a slump, fatigue, or simply being a below-average shooter.

Over large samples, shot making (the SQ Actual minus Expected gap) tends to regress toward zero for most players. The average NBA player converts shots at roughly the rate their shot quality predicts. But some elite shooters — Stephen Curry, Kevin Durant, Kyrie Irving — sustain positive shot-making margins over multiple seasons, suggesting genuine skill beyond what location and context predict. Identifying whether a player's hot streak is shot quality (they are getting better looks) or shot making (they are hitting tough shots) is critical for projecting whether the performance will continue.

Comparing a 30-point scorer's True Shooting percentage to a 10-point role player's is fundamentally misleading. The role player takes the easiest shots available — catch-and-shoot corner threes created by someone else, open layups off cuts, putbacks on offensive rebounds. They never have to create their own shot against a set defense or take a tough pull-up jumper with the shot clock winding down. Their shot diet is curated for efficiency.

The star scorer operates in a completely different universe. They face the best defenders, draw double teams, and must create offense when the play breaks down. As usage increases — meaning a larger share of team possessions end with that player shooting, turning it over, or going to the line — the available shots get progressively harder. The easy looks are already claimed. What remains are contested jumpers, drives into help defense, and isolation possessions against primary defenders.

This is the volume-efficiency curve: as shot volume increases, efficiency typically decreases. A player who scores 20 points per game on 60% True Shooting would almost certainly not maintain 60% TS at 30 points per game. The additional 10 points would come from harder shots against more defensive attention. This is why "just give them more shots" is usually not the answer — and why a role player's efficiency numbers are not evidence that they could be a star if given the opportunity.

Context-aware evaluation means weighting both volume and efficiency together. A star maintaining 58% TS on 30 points per game is providing more offensive value than a role player at 62% TS on 10 points per game, because the star is producing far more total scoring output on only slightly lower efficiency — and they are doing it against much tougher defensive coverage. The DataBallr PvP page lets you compare players side by side with both usage and efficiency visible, making this tradeoff explicit.

Getting to the free throw line is one of the most reliable and consistently undervalued offensive skills in basketball. Free throws are worth approximately 1.5 points per trip — with league-average free throw shooting at roughly 78%, and most foul trips sending a player to the line for two attempts, the expected value is 2 × 0.78 = 1.56 points. That alone exceeds league-average scoring per possession (~1.12), making every standard foul trip an efficient outcome.

The TS% formula uses a 0.44 multiplier per free throw attempt (TSA = FGA + 0.44 × FTA) to estimate the possession cost of free throws averaged across all situations. This factor is less than 0.5 because not all free throws consume a full possession: and-one free throws come after a made basket (the possession was already used by the field goal attempt), technical and flagrant free throws are bonus possessions that cost nothing, and three-shot fouls cost only one possession despite three attempts. These scenarios pull the league-wide average below 0.5 per FTA. Using this average, each free throw attempt produces ~0.78 points while consuming ~0.44 possessions, yielding roughly 1.77 points per possession equivalent — one of the most efficient scoring methods in basketball. This is why free throw generation inflates TS% well above eFG%.

Free throw rate, measured as FTA/FGA, quantifies how often a player draws fouls relative to their field goal attempts. A player with a 0.40 FT rate takes 40 free throws for every 100 field goal attempts — they are either aggressive driving to the rim, skilled at drawing contact, or both. This metric is tied to playing style and physicality rather than shot-making variance, which makes it one of the most stable offensive statistics from season to season.

The TS%-to-eFG% gap is the clearest indicator of free throw generation value. A player shooting 50% from the field with heavy free throw generation might have a 50% eFG% but a 58% TS%. The 8-point gap is entirely free throw value. Players like James Harden, Joel Embiid, and Giannis Antetokounmpo have historically shown massive TS%-eFG% gaps, reflecting their dominance at drawing fouls. When evaluating offensive efficiency, ignoring free throw generation misses a major piece of the picture.

Individual stats are always entangled with teammates. A player's assist numbers depend on whether they share the court with shooters who convert their passes. A player's defensive rating depends on the team scheme and the quality of their co-defenders. A player's offensive rating depends on whether their team has floor spacing or clogs the paint. Evaluating any player in isolation from their context is analytically incomplete.

WOWY (With Or Without You) analysis addresses this by comparing how a team performs across different player combinations. At its simplest, WOWY asks: what is the team's offensive and defensive rating when Player A is on the court, and what is it when Player A sits? But it goes deeper than basic on/off splits. You can layer the analysis: how does the team perform when Player A AND Player B are both on the court? What about when Player A is on but Player B is off? This reveals whether two players are better together or apart.

These comparisons expose synergies and redundancies that raw individual stats cannot capture. If a team's offensive rating jumps 6 points when two specific players share the court but drops when either plays alone, those players have a measurable synergy — their skills amplify each other. Conversely, if a team's offense is no better with both players than with either individually, their skills may be redundant — they are competing for the same possessions or occupying the same role.

The DataBallr WOWY page lets you toggle individual players on and off for any team and see how the team's offensive and defensive ratings shift across different lineup configurations. You can filter by season, season type, and leverage situations. Combined with the padding feature (which regresses small-sample lineup data toward league average), the WOWY tool provides a granular look at how players interact within lineup ecosystems — far more informative than evaluating any player in a vacuum.

Five-man lineup analysis has a fundamental data problem: combinatorial sparsity. An NBA roster carries roughly 15 players, yielding over 3,000 possible five-player combinations. In an 82-game season with about 100 possessions per team per game split across multiple lineup configurations, most five-man combinations play fewer than 100 possessions together. Many play fewer than 50. Some of the most interesting combinations — rare pairings during injury absences or late-game situations — might share only 20 possessions.

One hundred possessions is not nearly enough to draw reliable conclusions about a lineup's true quality. The standard deviation of point differential per 100 possessions is roughly 12-15 points for a single 100-possession sample. That means a true +3 lineup could easily register as -10 or +16 in any given 100-possession stretch. When the noise band is 5-10 times wider than the signal you are trying to detect, individual observations are close to meaningless.

This is precisely why RAPM uses player-level analysis rather than five-man lineup data. Instead of trying to evaluate thousands of five-man combinations with tiny sample sizes each, RAPM pools information across all lineups a player appears in. A player who appears in 15 different five-man lineups over a season contributes data from all of them to a single player-level estimate. This dramatically increases the effective sample size and produces much more stable ratings.

The practical implication is clear: when you encounter five-man lineup data showing a lineup with a +15 net rating over 80 possessions, resist the urge to declare it elite. That rating is well within the range of random variation. Use padding (regression to the mean) to get a better estimate, and focus on player-level or two-man combination data where sample sizes are substantially larger and more reliable.

Roster construction is not a fantasy basketball exercise where you stack the five highest-rated players. The best lineups are composed of players whose skills complement each other — each player provides something the others do not, and together they cover all the skills a lineup needs. Floor spacing, rim pressure, ball-handling, switchable defense, rebounding, and playmaking all need to be present, but they do not all need to come from the same player.

Spacing is the most visible example. A lineup with five non-shooters clogs the paint, eliminates driving lanes, and makes every half-court possession harder. A lineup with five shooters may stretch defenses but lacks rim pressure and offensive rebounding. The best lineups balance shooters and drivers — typically 3-4 capable shooters around 1-2 players who attack the basket. This is not a theory; it is directly observable in lineup-level offensive rating data across the league.

Shot diet interactions create subtler redundancies. When two high-usage ball-dominant guards share the court, each handles the ball less and takes fewer shots. Their individual production may decline even if the lineup performs adequately. This is not because either player got worse — it is because they compete for the same opportunities. One player's usage is partly at the expense of the other's. The lineup might be good, but it might be less than the sum of its parts.

WOWY data reveals these dynamics directly. If Player A's TS% drops 3 points when Player B is also on the court, that could signal skill redundancy — both players need the ball in the same situations, forcing one into a less comfortable role. If Player A's TS% rises when Player C joins the lineup, that suggests complementary skills — perhaps Player C's spacing opens driving lanes for Player A. The DataBallr WOWY and Stat Line Shift pages let you explore these interactions systematically.

Offensive rebounding has clear, quantifiable value. Each offensive rebound extends a possession, providing a second-chance scoring opportunity worth roughly 1.0-1.1 points per second-chance possession. Teams that dominate the offensive glass — securing 30%+ of available offensive rebounds — generate several extra possessions per game. Over a season, that can be worth 3-5 additional wins.

But offensive rebounding comes with a transition defense cost. Players who crash the offensive glass are, by definition, not getting back on defense. When the defending team secures the rebound and pushes in transition, the offensive rebounding team is outnumbered. Transition possessions are among the most efficient in basketball — typically worth 1.10-1.15 points per possession — so giving up transition opportunities is a real price.

Shot diet complicates the picture further. Three-point misses produce longer, more unpredictable rebounds that scatter farther from the basket. Two-point misses, especially from close range, produce shorter rebounds that are easier to secure. A team that shoots a high volume of threes will naturally have a lower offensive rebound percentage even with the same effort and personnel, because the rebound opportunities are fundamentally harder. This means comparing ORB% across teams or eras without accounting for shot mix is misleading.

The RAPM decomposition makes these tradeoffs concrete. Consider two players. Steven Adams has historically shown an oREB impact of roughly +4.0 — his offensive rebounding is elite — but his oTS impact is about -1.0, reflecting the spacing cost of having a non-shooter on the floor. Joel Embiid shows the inverse: oTS of roughly +3.0 (he is a shooting big who spaces the floor) but oREB of about -1.5 (he does not crash the glass as aggressively). Neither profile is inherently better; they are different tradeoffs. A -6.0 relative ORB% is not necessarily bad if the lineup's shot mix is weighted toward threes and long midrange shots — the rebound opportunities simply are not there.