Welcome to our NBA Player Stats page, your go-to destination for detailed performance metrics, real-time updates, and in-depth analyses of every player's game contributions; discover comprehensive stats and trends that highlight the impact of each player on the court, and be sure to check out our NBA starting lineups for a complete view of who’s making the plays.
Welcome to our NBA Player Stats page, your go-to destination for detailed performance metrics, real-time updates, and in-depth analyses of every player's game contributions; discover comprehensive stats and trends that highlight the impact of each player on the court, and be sure to check out our NBA starting lineups for a complete view of who’s making the plays.
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| PG | OKC | 86 | 33.8 | 32.7 | 6.3 | 4.9 | 1.7 | 1 | 2.5 | 0.9 | 2.1 | 5.7 | 8 | 8.9 | 2.2 | 34.6 |
| PF | MIL | 98 | 33.4 | 30.1 | 5.9 | 11.9 | 0.8 | 1.1 | 3.2 | 2.4 | 0.1 | 0.8 | 6.2 | 10.3 | 2.5 | 35.7 |
| C | DEN | 95 | 36.1 | 29.5 | 10.4 | 12.9 | 1.8 | 0.7 | 3.2 | 3 | 1.9 | 4.6 | 5.1 | 6.2 | 2.3 | 29.7 |
| PG | LAL | 94 | 34.5 | 27.8 | 7.8 | 8.2 | 1.9 | 0.4 | 3.8 | 0.8 | 3.5 | 9.8 | 5.9 | 7.6 | 2.6 | 34.9 |
| SG | MIN | 87 | 36.2 | 27.5 | 4.7 | 5.7 | 1.2 | 0.6 | 3.3 | 0.8 | 4.1 | 10.2 | 5.5 | 6.5 | 1.8 | 31.7 |
| SF | BOS | 92 | 36 | 27.1 | 6 | 8.6 | 1.1 | 0.5 | 3 | 0.7 | 3.6 | 10.2 | 5.1 | 6.3 | 2.2 | 31.6 |
| PF | PHO | 95 | 36.5 | 26.9 | 4.3 | 6.2 | 0.8 | 1.3 | 3.2 | 0.4 | 2.6 | 5.9 | 5 | 6 | 1.7 | 29 |
| PG | NY | 82 | 34.9 | 26.5 | 7.4 | 2.9 | 0.9 | 0.1 | 2.5 | 0.4 | 2.3 | 5.9 | 5.7 | 6.9 | 2.2 | 30 |
| PG | PHI | 86 | 37.1 | 26.1 | 6.2 | 3.3 | 1.7 | 0.4 | 2.3 | 0.3 | 3 | 9.2 | 4.8 | 5.4 | 2.2 | 30.2 |
| PG | DET | 83 | 34.5 | 25.9 | 9.1 | 6 | 1.1 | 0.7 | 4.5 | 0.8 | 2.2 | 6.1 | 4.6 | 5.3 | 2.8 | 33.3 |
| SG | PHO | 90 | 36.9 | 25.4 | 7.1 | 4.1 | 1 | 0.2 | 2.9 | 0.9 | 2.5 | 7.3 | 6 | 6.7 | 2.7 | 28.9 |
| PG | CHA | 86 | 31.8 | 25.4 | 7.2 | 5 | 1.1 | 0.3 | 3.6 | 1 | 3.8 | 11.5 | 4.2 | 4.9 | 3.3 | 35.8 |
| C | DAL | 89 | 33.5 | 25.1 | 3.5 | 11.7 | 1.3 | 2.1 | 2.1 | 2.8 | 0.7 | 2.1 | 5.9 | 7.4 | 1.9 | 30.2 |
| PF | ORL | 77 | 33.8 | 25.1 | 4.6 | 7.2 | 0.8 | 0.7 | 2.8 | 1.1 | 1.9 | 5.8 | 6.1 | 8.4 | 2.1 | 33 |
| PG | MIL | 88 | 35.5 | 25 | 7.1 | 4.7 | 1.2 | 0.2 | 2.8 | 0.5 | 3.4 | 9.1 | 6.2 | 6.8 | 1.6 | 28 |
| PG | DAL | 88 | 35.4 | 24.9 | 4.5 | 4.8 | 1.3 | 0.4 | 2.2 | 1.2 | 2.9 | 7.3 | 4.1 | 4.4 | 2 | 27.9 |
| SF | LAL | 97 | 34.4 | 24.8 | 8.5 | 8 | 0.9 | 0.6 | 3.9 | 0.9 | 2.2 | 5.8 | 3.6 | 4.6 | 1.4 | 31 |
| PF | NY | 90 | 34.3 | 24.6 | 3 | 12.6 | 1 | 0.7 | 2.5 | 3 | 2.1 | 4.9 | 4.7 | 5.6 | 3.4 | 27.3 |
| PF | NO | 88 | 28 | 24.5 | 5.1 | 7.1 | 1.3 | 0.9 | 3 | 2.5 | 0.1 | 0.4 | 5.4 | 8.2 | 2.8 | 34.8 |
| C | PHI | 95 | 30.4 | 24.4 | 4.4 | 8.4 | 0.8 | 1 | 3.4 | 1.9 | 1.2 | 4.2 | 8.1 | 9.2 | 2.3 | 35.1 |
| SF | ORL | 79 | 33.1 | 24.3 | 4.8 | 5.7 | 1.3 | 0.4 | 2.4 | 1 | 1.7 | 5.8 | 4.6 | 5.3 | 2.6 | 31.7 |
| PF | SA | 67 | 32.7 | 24.3 | 3.5 | 11.1 | 1.2 | 3.8 | 3.2 | 1.8 | 3.2 | 8.9 | 3.3 | 4 | 2.2 | 30.7 |
| PG | ATL | 91 | 35.5 | 24 | 11.4 | 3.1 | 1.2 | 0.2 | 4.8 | 0.5 | 2.8 | 8.4 | 6.4 | 7.4 | 1.9 | 29.9 |
| SG | BKN | 75 | 30.7 | 24 | 3.8 | 3.3 | 0.6 | 0.1 | 2.5 | 0.6 | 2.7 | 7.8 | 5.3 | 6 | 1.8 | 33.6 |
| SG | CLE | 87 | 30.7 | 23.9 | 4.8 | 4.3 | 1.3 | 0.2 | 2.1 | 0.8 | 3.3 | 8.9 | 4.1 | 5 | 2 | 31.5 |
| PG | GS | 97 | 31.6 | 23.6 | 6 | 4.4 | 1 | 0.4 | 3 | 0.5 | 4.2 | 11 | 3.9 | 4.2 | 1.4 | 29.5 |
| SG | MIA | 83 | 35.1 | 23.5 | 5.7 | 5.3 | 0.9 | 0.2 | 2.6 | 0.4 | 3.3 | 9 | 3.6 | 4.2 | 1.2 | 28.3 |
| PG | SA | 83 | 35.6 | 23.5 | 6.3 | 4.8 | 1.5 | 0.4 | 2.8 | 0.8 | 1.9 | 6.1 | 4.2 | 5.1 | 2.6 | 28.2 |
| SG | SAC | 89 | 34.3 | 23.2 | 4.3 | 4.5 | 0.8 | 0.2 | 2.8 | 0.3 | 3.1 | 7.2 | 3.3 | 4 | 1.4 | 25.8 |
| SG | BOS | 86 | 34.8 | 22.9 | 4.6 | 6 | 1.2 | 0.3 | 2.6 | 1.4 | 1.8 | 5.9 | 4 | 5.3 | 2.5 | 28.9 |
| SG | LAC | 79 | 32 | 22.7 | 2.1 | 3.4 | 1.3 | 0.2 | 2 | 0.4 | 3.1 | 7.4 | 4 | 4.8 | 1.8 | 26.7 |
| PF | MEM | 84 | 29 | 22.4 | 2.1 | 5.7 | 1.2 | 1.6 | 2.2 | 1.2 | 1.9 | 5.2 | 4.3 | 5.6 | 3.4 | 28.9 |
| SG | LAC | 88 | 34.7 | 22.3 | 8.8 | 5.9 | 1.5 | 0.7 | 4.4 | 0.7 | 3 | 8.7 | 6.5 | 7.4 | 2.1 | 29.7 |
| SF | TOR | 87 | 32.6 | 22.2 | 5.2 | 5.6 | 0.9 | 0.6 | 3.8 | 0.9 | 2.4 | 6.4 | 2.6 | 3.1 | 2.5 | 30.8 |
| SF | SAC | 88 | 35.5 | 22.2 | 4.3 | 3.9 | 0.8 | 0.4 | 1.4 | 0.6 | 1.1 | 3.3 | 4.8 | 5.5 | 1.9 | 24.8 |
| PG | MEM | 94 | 29.5 | 22.2 | 7.4 | 3.9 | 1.2 | 0.3 | 3.7 | 0.5 | 1.5 | 5.2 | 5.5 | 6.5 | 2.2 | 31.7 |
| PG | DEN | 85 | 35.8 | 21.6 | 6.1 | 3.9 | 1.4 | 0.5 | 2 | 0.7 | 2.4 | 6 | 3.3 | 3.8 | 2 | 24.1 |
| SG | HOU | 81 | 33.1 | 21.6 | 3.4 | 4.6 | 0.8 | 0.3 | 2.4 | 0.6 | 3 | 8.5 | 3.4 | 4.2 | 1.6 | 27.5 |
| SF | OKC | 71 | 32 | 21.4 | 5.3 | 5.4 | 1.7 | 0.7 | 2.2 | 0.8 | 1.8 | 5.1 | 3.3 | 4.2 | 2.4 | 26.8 |
| SF | CHA | 67 | 33.7 | 21.4 | 3.6 | 4.8 | 1.1 | 0.7 | 2.7 | 1 | 4 | 10.8 | 2.6 | 3 | 2.8 | 28.2 |
| SF | NO | 76 | 34.3 | 21.4 | 3.7 | 5.1 | 1.1 | 0.7 | 2 | 0.9 | 3 | 8.1 | 3.9 | 4.4 | 2.1 | 24 |
| SG | TOR | 85 | 32.3 | 21.4 | 5.4 | 6.4 | 0.8 | 0.2 | 2.9 | 1 | 1.9 | 5.3 | 3.4 | 5.4 | 2.5 | 28.5 |
| PG | NO | 84 | 32.1 | 21 | 4.2 | 3.8 | 0.8 | 0.5 | 1.9 | 0.7 | 3 | 8.2 | 2.2 | 3.2 | 2.2 | 27.3 |
| SF | CHA | 81 | 31.8 | 21 | 3.8 | 7.8 | 0.8 | 0.7 | 2.1 | 1.2 | 2.4 | 7.1 | 3.6 | 4.2 | 1.5 | 27.6 |
| PF | IND | 87 | 32.4 | 20.8 | 3.4 | 7 | 0.9 | 0.5 | 1.5 | 1.7 | 1.7 | 4.3 | 3 | 4.1 | 2.6 | 24.8 |
| PG | CLE | 88 | 30 | 20.8 | 6.6 | 2.8 | 1.2 | 0.2 | 2.4 | 0.6 | 2.9 | 7.1 | 3 | 3.4 | 2 | 27.5 |
| PG | WAS | 84 | 29.4 | 20.6 | 4.4 | 3.1 | 1.3 | 0.4 | 3.1 | 0.5 | 3.5 | 9.2 | 3.6 | 4.1 | 3 | 29.1 |
| SG | CHI | 75 | 32.8 | 20.3 | 4.5 | 3.6 | 0.9 | 0.2 | 2.3 | 0.3 | 3 | 8.3 | 3.5 | 3.9 | 2.1 | 24.6 |
| SF | LAC | 95 | 30.1 | 19.9 | 3 | 5.6 | 1.5 | 0.5 | 2 | 1 | 2 | 5 | 2.3 | 3 | 1.5 | 28.1 |
| SF | TOR | 83 | 33 | 19.6 | 5.9 | 7.8 | 1.5 | 1 | 2.9 | 1.7 | 1.2 | 4.5 | 3.8 | 4.9 | 1.9 | 27.2 |
Contents
NBA player stats shine a spotlight on individual performances, revealing how each athlete contributes to their team’s success (or struggles). Beyond raw points and rebounds, advanced metrics like Player Efficiency Rating (PER), True Shooting Percentage (TS%), and Usage Rate help paint a nuanced picture of a player’s value. By examining these numbers in the context of roster fit and game strategy, you can uncover insights that a simple box score might not provide.
Staying current on player stats is essential for bettors, DFS enthusiasts, and fantasy managers. Even a short-term scoring surge can signal a bigger breakout if paired with rising usage rates or better shooting efficiency. Conversely, a player putting up impressive numbers on unsustainably hot shooting may regress in the near future. Consistently monitoring these patterns will keep you ahead of casual observers who rely only on headline performances.
Different analysts lean on various statistical frameworks to evaluate NBA players. Traditional box-score stats—points, rebounds, assists—offer a quick read, but advanced metrics provide a deeper dive. For instance, PER (Player Efficiency Rating) summarizes a player’s per-minute productivity, while Win Shares estimate how much an individual contributes to team victories. Plus-Minus and On/Off metrics show how a squad fares with or without a certain player on the court.
Additionally, shot-tracking data (e.g., shot charts, catch-and-shoot percentages) can reveal whether a player thrives off dribble penetration or excels in spot-up opportunities. Understanding which metrics drive a player’s perceived value helps you evaluate if their production is sustainable or matchup dependent. You can also look at **defensive** advanced stats—like Defensive Box Plus-Minus (DBPM) or Deflections—to gauge impact beyond highlight plays.
While some players rack up gaudy numbers against weaker opposition, others maintain strong efficiency even when facing top-tier defenses. Evaluating player performance in marquee matchups—like prime-time games or clashes against elite defenders—can clarify whether recent output is merely a product of soft matchups. A guard who lights up a poor perimeter defense might struggle against a physical, well-coordinated unit, so factoring in quality of competition is key.
This context also matters for situational stats. Clutch scoring (points in the final five minutes of close games) and efficiency under pressure provide clues about a player’s poise. Similarly, game tempo impacts raw numbers; a fast-paced opponent can inflate counting stats like points and rebounds. Adjust your assessment by considering how each player’s skill set meshes with the pace and defensive style of the teams they face.
Player stats can fluctuate dramatically when key teammates are sidelined, offering increased usage for the healthy ones. A second option might temporarily become the primary scorer, boosting shot attempts and point totals. Conversely, a player who thrives off a certain teammate’s playmaking could see his own production dip if that teammate is out. Tracking roster health and changes in starting lineups helps you interpret short-term statistical spikes or slumps.
Depth charts also factor in. A deep team can spread around touches, capping individual stats, while a top-heavy roster may feature players with sky-high usage rates. By recognizing which situations boost or reduce a player’s stat production, you can adapt your fantasy and betting strategies accordingly. Look for evidence—like increased pick-and-roll possessions or more isolation sets—to validate whether a stat bump truly reflects an expanded role.
Player stats heavily influence prop bets (e.g., total points, rebounds, assists) and can affect broader markets like point spreads or over/unders. If a star is on a hot streak, sportsbooks may inflate their scoring line, potentially creating opportunities for savvy bettors who see signs of regression. On the flip side, a player stepping into a bigger role might be undervalued if the public hasn’t caught on to emerging trends.
Bettors often correlate a team’s success with a player’s statistical output—e.g., if a particular guard consistently dominates weaker perimeter defenders, taking the over on points (and possibly the team’s overall scoring) could be profitable. Monitoring how stats shift when the player faces elite defenses or tough road environments can help you gauge the true odds of repeating a strong performance.
Prop bets for points, assists, or rebounds typically hinge on a player’s recent output and average usage. If a forward’s rebounding numbers spike after an injury to the team’s starting center, oddsmakers may eventually catch up, but there’s often a lag. Capitalizing on that gap can net quick gains—especially if advanced metrics show that the forward’s rebound rate is sustainable (e.g., an uptick in box-outs or contested rebound percentages).
Conversely, if a perimeter scorer’s three-point percentage is skyrocketing due to favorable matchups, you might expect a dip against stronger wing defenders, making the under a tempting proposition. Tracking shot selection—like attempts at the rim vs. mid-range jumpers—also reveals how stable a player’s efficiency might be moving forward.
Short-term stats can be deceptive. A player might average 25 points over a week on unsustainably hot shooting or feast on subpar defenses. By reviewing advanced data—like shot quality or free throw rate—you can determine whether a breakout is legitimate or a mirage. If a guard’s assist totals jump due to inflated minutes, that uptick may not last when the rotation normalizes.
Alternatively, a mild slump in scoring could hide other improvements, such as a higher assist percentage or lower turnover rate, signaling an eventual return to form once the shooting regresses to the mean. Spotting these underlying trends before the betting public does can yield value across the prop markets. It can also help you avoid overreacting to superficial cold streaks or surges.
When you leverage player stats to spot favorable prop bets or lines, sportsbook promos can enhance your potential returns. Common offers include odds boosts, risk-free bets, or parlay boosts that multiply your winnings if you combine multiple prop wagers. Such promos help offset risk if a hot-shooting guard reverts to the mean sooner than you anticipated.
Player stats aren’t static—they can spike if a veteran rests, or dip if a key ball-handler returns from injury. DFS prices and prop lines may not adjust quickly enough, creating windows of opportunity. If a newcomer thrives in an expanded role, their DFS salary might remain low for a game or two, and prop bets could undervalue their scoring or rebounding potential. Conversely, a returning star’s presence could siphon touches from a bench scorer, deflating that player’s fantasy value.
Matching your predictions against real-time data is crucial. If a player’s usage climbs in the first quarter, indicating the coach has shifted more offense through them, in-game DFS swaps or live prop bets might be especially appealing. Being nimble and informed can amplify your edge.
Some players thrive against specific defensive schemes. For example, a pick-and-roll maestro might feast on teams lacking disciplined rim protection, while a spot-up shooter could excel if a defense overcommits on help rotations. Reviewing a player’s track record against various lineups can reveal under-the-radar DFS gems—like a backup forward who punishes smaller lineups.
Additionally, check synergy data for two-man lineups. If a center consistently sees increased touches whenever a certain playmaking guard is on the floor, that synergy could boost daily fantasy returns. You can also monitor game pace: players reliant on fast-break points or transition threes may underperform in grind-it-out matchups.
As a game progresses, foul trouble or in-game injuries may thrust bench players into bigger roles. A scorer who typically sees 25 minutes might get 35 if the starter picks up early fouls. Recognizing such shifts early can yield profit on real-time props—like total points or rebounds for the newly elevated player.
On the other hand, a star might get shut down if the score margin becomes lopsided, minimizing second-half production. Savvy bettors and DFS managers track these in-game developments, balancing a player’s upside with the risk of limited minutes. If you anticipate blowouts, consider focusing on the role players who benefit from extended garbage-time usage.
Season-long fantasy basketball hinges on player performance over weeks and months, making it vital to track evolving stats. A shooter stuck in a slump might be a prime buy-low target if advanced metrics suggest their form remains solid. Meanwhile, a bench player climbing the depth chart could maintain expanded minutes all season if they keep delivering consistent per-minute production.
Factor in roster construction too. If you’re heavy on scorers but lack assists, seeking out players who’ve upped their playmaking duties (perhaps due to a coaching adjustment) can balance your categories. The best fantasy managers blend short-term momentum with long-term trends in advanced stats to create a balanced yet dynamic roster.
A second-year forward or rookie guard can break out when their team shapes a new offensive system around their strengths. By comparing raw box scores with advanced data—like Usage Rate or Assist Percentage—you might detect a future star’s upward trajectory. If their improvement coincides with lineup changes or an injury to a teammate, you have a chance to scoop them off waivers before the rest of your league notices.
Be mindful, however, of short-term anomalies. Sometimes high shooting percentages or inflated block rates are unsustainable. Dig into shot-location data or defensive scheme influences to verify whether that production is likely to continue. If you sense regression, consider selling high via trade offers while the hype is strong.
Coaches regularly shuffle lineups to spark better results, which can drastically shift player stats. An athletic wing transitioning from the bench to the starting five might see a jump in scoring if they pair well with the lead guard, while a high-usage bench scorer might lose touches next to a ball-dominant superstar. Keeping tabs on local beat reporters and postgame quotes can give you an early heads-up when a coach plans to alter rotations.
Those who spot rotation changes first can exploit them in fantasy. For instance, if a marginal role player is rumored to get starter-level minutes, a speculative add could pay dividends if the coach’s plan stays intact. Conversely, a once-solid contributor might fade if their coach shifts them to a diminished role in favor of a defensive specialist.
Hot or cold streaks sometimes correspond to a series of matchups against specific defensive styles. If you see that a guard’s recent scoring surge came against teams weak in perimeter defense, they might slow down against tougher wing stoppers. Evaluate upcoming schedules to gauge which streaks could sustain or fizzle.
In deeper leagues, slight scheduling advantages—like extra back-to-back sets—can boost a fringe player’s cumulative stats over a week. If you notice a high-tempo opponent coming up, you might stash a bench big who’s adept at collecting boards in transition-heavy contests. Awareness of both schedule quirks and a player’s underlying skill set maximizes your fantasy roster’s efficiency.
NBA player stats extend far beyond the scoreboard, revealing nuances of a player’s role, efficiency, and impact that casual fans might overlook. Interpreting these metrics effectively can elevate your strategy in betting markets, DFS competitions, and fantasy leagues. By factoring in advanced statistics, you uncover patterns that signal growth or regression, allowing you to make decisions rooted in data rather than hype.
A star’s flashy scoring average might obscure defensive liabilities or poor shot selection, while a seemingly modest stat line could hide an elite role player who excels in efficiency or secondary creation. By peeling back the layers of raw totals, you gain clarity about each player’s contributions—and how those might evolve under shifting team dynamics. Staying curious and up-to-date on advanced player metrics is key to keeping pace in the ever-evolving NBA.
Some NBA legends delivered staggering stat lines that stood the test of time, either through sheer volume or unmatched efficiency. Their success often stemmed from a combination of skill, opportunity, and a system tailored to their strengths. Modern-day analytics can illuminate why these icons were so dominant and how their methods might translate—or not—in today’s game.
Jordan’s athleticism and scoring instincts were complemented by a relentless drive that propelled him in clutch situations. Modern analytics highlight his killer mid-range game and innate knack for drawing defenders, fueling assists and open shots for teammates. By studying his shot distribution and pacing, today’s analysts glean insights into how unwavering confidence and skill can shape an all-time great’s statistical profile.
His scorching efficiency from beyond the arc, combined with high-volume attempts, redefined how teams approach perimeter defense. Modern shot-tracking data shows how Curry’s gravity bent defensive schemes, opening up easy cuts and short rolls for the Warriors’ supporting cast. This synergy underlines how one player’s unique skill can drastically reshape on-court dynamics and transform a franchise into a juggernaut.