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“Advanced” Stats Tutorial, Part 3 – Team Defense & Overall Player Value
In Part 3 of this Advanced Stats Tutorial, we examine the statistics related to a team’s overall defensive ability, and then look at a couple all-encompassing player evaluation tools.
In case you missed Part 1 where I addressed the main statistical categories that pertain primarily to scoring, and Part 2 where I examined the statistics related to rebounding, passing, and ball-handling, you can find them linked below.
Opponents’ Turnover Rate (Comparable “Traditional” Stat: Opponents’ Turnovers per game)
Explanation: On a player level, steals may be a more sought after statistic, but from an entire team perspective, opponents’ turnover rate is much more useful. This stat weighs the number of opponents’ possessions that end in a turnover instead of looking at a pace-unadjusted number such as turnovers per game. In the 2011-12 NBA season, the league average TOR was 13.8%.
Example of Impact: In the 2011-12 season, the Clippers ranked right around the league average in opponents’ turnovers per game (16th) with 14.6, but jump up to 9th in opponents’ turnover rate at 14.2%. Given the fact that the Clippers played at one of the slowest paces in the NBA last year, it makes sense that their per-game average would not be an accurate representation of their ability to create turnovers on defense.
2011-12 Hornets: The Hornets finished 19th in the NBA in opponents’ turnover rate at 13.54%, just a bit under the league average. With their newly re-tooled roster, the team may find itself in the bottom 10 this season; however, the next stat is one that could help to make up for it.
Opponents’ Effective Field Goal Percentage (Comparable “Traditional” Stat: Opponents’ FG%)
Explanation: Instead of looking at how well a team’s opponents shot against them overall, this stat simply weights their field goal percentage based on the value of the shot (2-pointer vs. 3-pointer). Doing so more accurately portrays team defensive ability; for example, a team may allow a relatively low field goal percentage, but if a lot of those made shots are coming in the form of threes instead of twos, the defense could be worse off. In the 2011-12 NBA season, the league average eFG% was 48.7%.
Example of Impact: Last season, the Clippers ranked 14th in opponents’ FG% at 44.7%; however, they were fourth worst in the NBA at defending the 3-pointer, resulting in an opponent eFG% of 49.2%, only 20th in the league.
2011-12 Hornets: The Hornets posted an opponents’ eFG% of 48.53%, good for 13th in the NBA and slightly better than the league average. With Anthony Davis protecting the paint and a healthy Eric Gordon on the perimeter, this number very well could improve this season.
Player Efficiency Rating (PER)
Explanation: I could not even begin to try to explain PER as well as the creator himself, so I’ll let John Hollinger’s words do it:
The player efficiency rating (PER) is a rating of a player’s per-minute productivity. To generate PER, I created formulas that return a value for each of a player’s accomplishments. That includes positive accomplishments such as field goals, free throws, 3-pointers, assists, rebounds, blocks and steals, and negative ones such as missed shots, turnovers and personal fouls. Two important things to remember about PER are that it’s per-minute and is pace-adjusted. Bear in mind that PER is not the final, once-and-for-all evaluation of a player’s accomplishments during the season. This is especially true for defensive specialists — such as Quinton Ross and Jason Collins — who don’t get many blocks or steals. What PER can do, however, is summarize a player’s statistical accomplishments in a single number. That allows us to unify the disparate data on each player we try to track in our heads (e.g., Corey Maggette: free-throw machine, good rebounder, decent shooter, poor passer, etc.) so that we can move on to evaluating what might be missing from the stats. I set the league average in PER to 15.00 every season.
2011-12 Hornets: The top three Hornets in PER last season (among those who played more than 15 games) were Carl Landry (18.28), Jarrett Jack (17.98), and Gustavo Ayon (16.79). On the other end of the spectrum, the bottom three were Xavier Henry (9.24), Lance Thomas (10.15), and Al-Farouq Aminu (10.62).
Explanation: Simply put, win shares is a method of determining the amount of wins out of a team’s total for which each player was accountable. For a much more detailed explanation, check out Basketball Reference’s excellent breakdown. Win shares are broken down into offensive and defensive, and combined to get a total. Offensive win shares are generally accepted as a much more telling stat than defensive win shares, purely because defense is too team-driven and it’s difficult to quantify what each individual actually contributes.
Since win shares are heavily influenced by the amount of minutes played while gaining those win shares, WS/48 normalizes the raw influence of court time. It just inflates the win share total as if the player performed as recorded for 48 minutes per game rather than the actual number of minutes played.
Not all minutes are equally difficult to perform well in for all players, as a good player may perform well matching up against bench players for a portion of his time, like Jason Terry with the Mavericks or Lamar Odum with the Lakers, compared to how he does when facing starters, but this is a step in the right direction.
An average player has a WS/48 of just under 0.1.
2011-12 Hornets: The top three Hornets in win shares last season (among those who played more than 15 games) were Jarrett Jack (3.8), Gustavo Ayon (3.2), and Carl Landry (2.9). Conversely, the bottom three were Xavier Henry (.2), Chris Kaman (.7), and Lance Thomas (1.2).
Our hope at Hornets247 is that, after reading all three of these pieces on the utilization of advanced statistics in the NBA, Hornets fans all over can more properly evaluate and analyze the game of basketball that we all love so much. I hope you have enjoyed this tutorial, and I would be more than happy to answer any questions that anyone may still have about any of the metrics that I have presented over the past few days. For a “quick-hitter” rundown of these stat categories, be sure to check out my “Advanced Statistics Abbreviations Glossary.”