Are there metrics for defensive performance in basketball

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• The article titled “Basic Advanced Stats Guide” talks about how advanced stats have made it easier for NBA fans to judge play performance, which players or teams are over/underrated, and much more.
• The author of the article mentions that people like to deride advanced stats because they don’t understand them.
• The article goes on to describe different metrics and tools used to evaluate players and teams:
  • Pace: measures how many possessions a team uses per game.
  • Offensive Rating: measures how many points a team scores per 100 possessions.
  • Defensive Rating: measures how many points a team allows per 100 possessions.
  • Net Rating: Offensive Rating minus Defensive Rating; reflects a team’s overall quality better than just wins/losses.
  • True Shooting: accounts for the fact that three-point shots are worth more than two-point shots; also includes how many fouls a player draws.
  • Shot Locations: displays where a player shoots from on the court.
  • Total Rebound Percentage: divides the amount of rebounds a player gets by the number of total rebounds in a game, adjusting for the pace of teams and the differences in players’ minutes played.
  • Assist Percentage: estimates what percent of teammates’ baskets scored were assisted by the player in question.
  • Usage Percentage: calculates what percent of a team’s possessions a player used when he was on the floor.
  • Turnover Percentage: accounts for how often a player turns the ball over when handling it by adjusting for minutes played and pace.
  • Lineup Data: shows what 5-man, 4-man, 3-man, and 2-man units played the most for each team and how well they performed.
  • On/Off: displays a player’s impact on his team when he’s on the floor, but requires careful consideration to avoid over-reliance on data.
  • Synergy Stats: provides insight into which play types a player is good at offensively and defensively.
• The author notes that there are many other stats not covered in this article.
• Several commenters on the post had questions or a desire for more information on specific stats or interpretation of the given statistics.
• A glossary of basketball statistics, equations, and other terminology is available at basketball-reference.com.

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• There are different types of advanced basketball metrics that can be used to evaluate player performance.
• In general, these metrics can be classified into two categories: box score-based metrics and impact-based metrics.
• Box score-based metrics use traditional box score stats to estimate a player’s contribution to winning, while the goal of impact-based metrics is to assess a player’s actual impact on the game itself, which may include factors not observable in the box score.
• The most well-known advanced stats in the box-score category are PER, WS, WS/48, BPM, VORP, and WARP. Among these, BPM has been completely reinvented since 2017, but is available in various forms on basketball reference. PER is generally considered outdated, while WS is decent but only meaningful for older seasons that are not covered by modern all-in-one metrics. More importantly, box score-based metrics can be problematic in that they can overvalue individual performance over team success, leading to a skewed assessment of a player’s true value.
• Impact-based metrics include all the different plus-minus stats out there, which today includes RPM, PIPM, and RAPM/xRAPM. Some newer and promising options are EPM and DPM. These metrics generally try to measure how much the team’s scoring margin changes when a particular player is on the court versus when that player is off the court, taking into account factors such as teammate quality and opponent strength. Because of this focus on overall impact on team success, these metrics can help identify players who may not have eye-popping statistics but nevertheless contribute significantly to winning.
• Advanced stats must also be evaluated for their use case and sample size. Metrics where on/off plays a big role need larger sample sizes to stabilize. While some all-in-one metrics can be unstable with a single season or less, others may require larger sample sizes.
• RAPTOR, PIPM, RPM, and BPM (VORP) are popular modern all-in-one metrics. RAPTOR has a database going back to ‘14, while RPM by ESPN now goes back to ‘03. Fans should be aware of these different types of metrics and their strengths and limitations.
• LEBRON and PT-PM are two newer metrics. LEBRON is largely untested, while PT-PM was promising but was never popular and isn’t updated anymore. Ben Taylor has a type of BPM that is pay to access and is reputable, although it is rarely referred to.
• Arguably the best “advanced stats”

• There is no consensus regarding a best way to measure defensive performance in basketball games.
• Several users suggest that closely watching games and team defensive ratings are the most efficient ways to assess defense.
• Metrics such as defended FG%, on/off defensive rating, rim protection DFG% and DBPM are suggested as good metrics for measuring defense.
• Some users note that defensive metrics can sometimes be noisy, have outliers, and won’t show small differences between players.
• Discussion around what constitutes the ideal statistic, measurement, or analytical tool is occasioned by the inability of defensive advanced basketball statistic to provide the same level of detail that offensive ones do.
• Metrics such as defensive rating, defensive win shares per 48 minutes, real defensive plus-minus and ESPN’s defensive real plus-minus have been used to quantify defensive performance, although no single metric has been uniformly adopted as the most accurate indicator.
• Some suggest that using a combination of metrics such as contested shots, box plus minus, hustle stats, and individual matchup data can provide a more holistic picture of defensive performance.
• Other stats that come up are opponents TOV percentage, RPM, PIPM, RAPTOR, DPIPM, DRPM and DRAPTOR.
• In response to “points against while on the court” being suggested as a metric for assessing defense, a user notes that this is not a good method because a single mismatch can cause a teammate to appear as though he’s been cooked.
• Another user suggests that opponents FG% when guarded by defender is a potentially good defensive metric.
• One user suggests that a good way to test the accuracy of a metric is to compare the statistic with how the player rated on various ‘scouting reports’ or when compared to the outputs of other advanced metrics.
• Yet another user notes that playing with great defenders in every other position can result in a player being hunted and abused and thereby give the impression that he’s only slightly above average on defense even when he’s smart and consistent at defending.
• There is an argument about peak vs consistent effort when it comes to defense performance and the value of reading passing lines compared to on-ball defense.
• Some also note that not all defenders will be put to the task of containing the best offensive players in games.

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• URL: https://www.reddit.com/r/nba/comments/u6pa2z/a_final_objective_look_at_the_202122_defensive/
• Title: “A final objective look at the 2021-22 Defensive Player of the year race.”
• Date: 1 year, 7 months ago
• Subreddit: r/nba
• Upvotes: 54 points
• External URL: https://imgur.com/a/XucPWgF
• Explanation of the webpage’s content:
  • The webpage analyzes defensive metrics in basketball and how well they capture defensive performance.
  • The first graph evaluates a player’s impact on defense by contesting shots, using defensive field goal attempts (DFGA) and DFG% Differential to adjust for the type of shots contested. A DFG% of 5 means a defender forces his opponents to shoot 5% worse than expected.
  • The second table uses “catch-all” defensive metrics to capture a player’s overall impact on defense. DWS represents a portion of games won from a player’s individual defensive contributions, while DBPM is a box score estimate of the points per 100 possessions saved vs. a league-average player. D-Raptor measures the number of points a player contributes to defense per 100 possessions, regardless of box score stats.
  • The third graph evaluates team performance defensively as a function of points scored per 100 possessions, on and off the court, to assess a player’s impact and evaluate which players are impacting their teams the most.
  • Observations and takeaways from the webpage include:
    • Draymond Green would’ve been a deserving winner if he had played the entire season.
    • Impact metrics and data fail to capture the leadership that players like Marcus Smart provide to establish a team’s identity.
    • Mikal Bridges’ impact is warped by the significant amount of time he plays against star players.
    • Rudy Gobert is objectively still the most impactful defender in the NBA.
    • Players like Jarrett Allen, Jaren Jackson, Robert Williams, and Bam Adebayo are expected to dominate the conversation for the next bunch of years.
    • Giannis had a great defensive season despite missing his starting center.
  • Comments on the page include:
    • Jarrett Allen, Jaren Jackson, and Robert Williams, as well as Bam Adebayo are going to dominate this conversation for the next bunch of years (37 karma).
    • Smart seems poised to win it with the most 1

• Defense is complicated, and measures that are used to evaluate a player’s defensive contributions to a team can be difficult to interpret and have limitations.
• There are several types of defensive statistics and metrics to evaluate a player’s contribution to defense in basketball.
• Box Score Stats are counting statistics available on any website, following is their description:
  • Steals: Credited to players who initiate the action that leads to the offense’s ball turnover while demonstrating active hands or good understanding of where the ball will end up on the next pass.
    • Strengths: Steals can help measure a player’s ability to generate transition offense.
    • Weaknesses: Steals can result from risky defense that can lead to easy scoring opportunities on unsuccessful tries.
  • Blocks: Deflecting an opponent’s legal field goal attempt.
    • Strengths: Blocks help in preventing easy baskets and have a deterrence effect on future basket attempts. Blocks take place on close field goal attempts, which are much higher % opportunities than other shots, and hence taking them away is very valuable.
    • Weaknesses: All blocked shots do not result in turnovers, and if a block misses the ball or goes back to the shooting team, it can result in another easy basket. Moreover, chasing blocks can also leave players out of position and expose them to easy driving angles and open shooters.
  • Defensive Rebounds: Collecting the ball after a miss by the opposing team.
    • Strengths: Good defensive rebounders can prevent second-chance opportunities, some of the highest percentage offensive chances in the game.
    • Weaknesses: Measuring a player’s defense by using defensive rebounding completely ignores the circumstances leading up to a basket and the importance of the first chance, given that preventing it prevents second chances.
• Advanced Box Score Stats supplement box score stats but cannot be found in standard ones; following is their description:
  • Deflections: Intercepting a live ball with hands and disrupting its intended target.
    • Strengths: Disrupting offenses, resulting in less time on the shot clock, deflections can often cause opponents to adjust their passing angles to avoid defensive wingspans.
    • Weaknesses: Like blocks, deflections can sometimes support offense by random cannoning of the ball. Similarly, like steals, gambling for deflections can lead players to be out of position and expose easy driving angles or open shooters.
  • Opponent’s Field Goal %: Measures the percentage of opponent shots scored while the defensive player was the active

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Relevant: True Importance: 9 Notes:

• The post asks for metrics used to grade a player’s defense in basketball and users in the comments suggest different metrics

• Some of the suggested metrics mentioned are:

  • Adjusted plus minus data
  • D-DPM (Defensive-DRIBBLE)
  • D-DRIP (Defensive-Dynamic Rim Protection)
  • DEPM (Defensive-EBPM)
  • D-LEBRON
  • 3 year D-RAPM (Regularized Adjusted Plus-Minus)

• Some users point out that relying solely on stats has limitations and emphasize the importance of the eye test as well as understanding the context of a player’s role and defensive scheme

• Users disagree on the effectiveness of certain metrics and players identified as good defenders:

  • Some users argue that Giannis, Allen, and Bridges should be included on any All-Defensive team and that some metrics are flawed for not including them
  • Other users argue that Bridges is a solid but overrated defender and that Covington has been an elite defender this year
  • One user argues that Kyrie is not a good defender, while another user suggests that he is a decent defender for his physical tools and one-on-one positions
  • Some users mention the importance of how a player ranks highly in multiple basic and advanced stat categories when evaluating defensive performance

• Users discuss other factors that need to be considered when evaluating a player’s defense:

  • Specific skills such as protecting the rim or defending against point of attack
  • Adapting to different defensive scenarios and understanding team schemes
  • The players and positions a player is defending against, as well as their physical tools like strength, speed, and size

• Some users express frustration with the limitations of stats in evaluating defense but still acknowledge their usefulness as a guide

• One user suggests focusing on communication, team, and off-ball defense to better understand defense through the eye test. Another user suggests that Curry is a more intelligent defender than his reputation suggests

• Some users argue that players who are effective at helping shelter a worse defender on their team may have lower defensive numbers, and that considering this in context can be helpful in evaluating defense

• There are disagreements on which players are deserving of being considered elite defenders, suggesting that there is still some debate on what makes a good defender.

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• The webpage discusses the challenge of finding advanced stats or metrics to measure defense in basketball.
• Kirk Goldsberry, an AMA, says there are no good advanced stats for defense.
• Reddit user raises the question of what stats best measure defense.
• Several Reddit users suggest that teams have proprietary analytics that are not public.
• A Reddit user with professional experience as an Xs & Os analyst and data analyst shares what they use to analyze a player’s defense, including context stats, interior defense, perimeter defense, defensive rebounding, and impact stats.
• The user also suggests that watching games live is not reliable and that best practice for a more holistic approach is to use contextualized data along with film analysis.
• The BBall Index provides stats and is user-friendly since it contextualizes other stats and provides a grade based on percentile.
• The BBall Index stats listed in user analysis include defensive roles, matchup difficulty, defensive versatility, defensive position, rim deterrence, dFG% at the rim against vs expectations, % of shots taken at the rim contested, adjusted rim points saved per 75 possessions, block rate on contests, passing lane defense, pickpocket rating, loose ball recovery rate, adjusted box-out rate, adjusted DReb success rate, DReb success rate + or -, adjusted TOV rate, D-LEBRON, and D-Raptor.
• The user indicates that context is vital as it impacts results; individual talent may not reflect the role players have on a team, and position and opponent difficulty can all factor into stats, hence the importance of contextual data.

In summary, the webpage consists primarily of a Reddit post with several users discussing the difficulty in finding metrics to evaluate defense in basketball. One user with professional Xs & Os analyst and data analyst experience shares the stats they use to evaluate defense and suggests that traditional ways of observing games is not reliable. The user also recommends using contextualized data and film analysis to provide a more holistic approach to evaluating defense in basketball. Finally, the BBall Index is introduced as a resource that lists contextualized stats and grades. Even though advanced stats for defense may not be publicly available, the conversation provides different perspectives on the importance of context and contain some recommended stats for evaluating defense in basketball.

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