Assessing NBA Scoring Champions Relative to League Average

A Historical Study

by Aqeel Phillips

With just a few weeks left in the regular season, some of us are left without much to root for anymore. HEAT fans remain optimistic in the surprisingly competitive battle for the first seed, and Suns, Mavs, and Grizzlies fans are biting their nails short in hopes that their teams can grab a playoff spot. However, a good percentage of us basketball fans now realize we have little to root for anymore (or if you’re a Sixers fan like me, you realized in about August), and are just waiting to see the final playoff seedings and end-of-season awards before the playoffs get underway. Besides the MVP, one of the most notable awards each year is the Scoring Title. Last season, we were treated with a thrilling ending as the battle for the Scoring Title came down to the wire between Kevin Durant and Carmelo Anthony.

This season, Kevin Durant aka the Slim Reaper has made things less interesting, currently scoring 32.2 points per game (PPG) over 2nd place Melo’s 28.0 PPG. Durant is the only player to average 30 points since he did in the 2009-10 season. The NBA has had a notable drop in scoring lately, a trend first starting when hand checking was instituted in the early 2000’s and extended as many teams have embraced sharing the ball throughout the team in order to better find open looks, namely threes, rather than relying on singular scorers. Durant’s current season widens eyes at first glance — averaging 4 points more than his next closest competitor will do that. But I find that PPG by itself doesn’t tell the full picture. Elgin Baylor averaged over 38 points in 1961-62, but that was over 50 years ago in a completely different league. So who had the most impressive season: 2014 Durant? 1962 Baylor? 2006 Kobe? We’ve witnessed plenty of monstrous seasons, and this study examines them in relation to the rest of the league at the time to contextualize the simple PPG marks.

League Scoring Average (Season)

To get a better comparison between scoring performances, we can divide a player’s PPG by their minutes per game (MPG) marks to see how they’re scoring with regard to the opportunities they’re being given. This is especially useful in calculating a league average scoring mark. We don’t want end bench players that average 0.6 PPG to drag down the entire league scoring average, most importantly because they outnumber the talented, 20+ PPG scorers in the league. Dividing PPG by MPG for each player across the league levels the playing field, and also accounts for the possibility that in any given season the league as a whole significantly played more or less bench/low-scoring players for whatever reason (for example, in the ‘60s there were much fewer players in the league and more minutes and points to go around).

For reference, here are the Points Per Minute values for the current league leaders in scoring:

League Leaders

(For those wondering about a full list of the league leaders in PPM, see the appendix)

In terms of points scored per time played, you can see that Durant is not just scoring at an average rate while playing more minutes, he is scoring more efficiently than the players below him on the list (shown by a higher PPM value than his competitors). It’s interesting to note that Melo averages more minutes than Durant, but Durant makes much better use of his time, scoring-wise, than Melo (Durant is also more efficient with his shot attempts – averaging 20.7 field goal attempts per game to Melo’s 21.5). This gives more evidence to Durant’s case for “best scorer in the league” – not only does he have the sheer output, but he also has the efficiency.

Next, we’ll calculate the average PPM value for the entire league, and compare each individual player to that average, to see how much better they score than the average replacement.

Unlike other studies I’ve done, I haven’t artificially subtracted out all of the players that aren’t contributing much (<20 MPG, <30 GP in previous articles), as using PPM should even out all contributions.

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Using Weighted Player Efficiency Rating to Predict the NBA Playoffs

By Neil Rangwani

This time of year means a few things in the world of sports: March Madness highlights take over ESPN, baseball stadiums start to fill up, and Knicks fans await their inevitable disappointment.

This NBA season looks remarkably competitive: the top of the league is crowded with legitimate contenders. The defending champion Heat and the Pacers, although sliding a bit recently, look to be the favorites in a weak East, while the Thunder, Clippers, and an extremely hot Spurs team each look like they could win the West.

In order to take a closer look at the playoff picture, we wanted to rank teams according to a metric that took into account various facets of a player’s game, so we decided to calculate a team equivalent of Player Efficiency Rating (PER). We took a relatively simple approach, since PER encompasses a number of basic statistics.

Introducing Weighted Player Efficiency Rating (WPER)

Using data for each player over the past four NBA seasons, we weighted each player’s PER by their playing time as a fraction of their team’s total playing time in order to account for a player’s actual usage. Then, we found each team’s Weighted Player Efficiency Rating (WPER) by summing the values for each player on each team.

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Stanislas Wawrinka – His Rise in Mental Fortitude

by Harold Li

In arguably the two best matches of 2013, Stanislas Wawrinka pushed Novak Djokovic to the limit. After an epic match point that has been seen over 200K times on YouTube, Wawrinka lost 12-10 in the fifth after 5 hours at the Australian Open. Eight months later, Wawrinka would lose to Djokovic again at the US Open Semi-Finals after leading two sets to one and winning just as many points as the Serb. After the match, Wawrinka was quoted saying, “At the Australian Open, I had to play my best level all the match to stay with him. [At the US Open], when I was playing my best level, I was better than him.”

In the most anticipated quarterfinal matchup at the 2014 Australian Open, another five-set battle ensued between the two titans, and it was Wawrinka that prevailed 9-7 in the fifth. This propelled him to his first grand slam title by overcoming Rafael Nadal in the final.

What changed? What got Wawrinka over the top? What made Wawrinka “better than [Djokovic]” on the day? By analyzing his three matches against Djokovic in the past 12 months and his Australian Open run, we outline the several key statistics in these matchups that show just how much Wawrinka has improved mentally to become the first player outside the Big 4 to win a Grand Slam since 2009.

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MLB Unveils Field Tracking System at Sloan Sports Analytics Conference


By Patrick Harrel

A few years ago, NBA teams started installing the SportVU system in their stadiums to get proprietary player tracking data and an edge over the competition, a decision that cost them $100,000 a pop. In the run-up to the 2013-14 campaign, the rest of the league caught up, making the tracking system standard and releasing the data to the public. Today at the MIT Sloan Sports Analytics Conference, Major League Baseball released their plan for a counterpart system, unveiling a player tracking system of their own.

This system has been in the pipeline for a while, with a pilot setup being deployed at Citi Field last year. This season, the system will expand to three stadiums, with all 30 MLB ballparks receiving the technology for 2015. Major League Baseball has been making a push to improve their technology in recent years, with PITCHF/x being released to the public years ago, giving us greater access to detailed pitch data.

Quite simply, the system looks beautiful. Check out this sample video the MLB released of Jason Heyward making a game-winning catch against the Mets last year. 

Ultimate Zone Rating and Total Zone Rating have advanced the field of defensive statistics, but they have their problems as they struggle with defensive shifts and do not differentiate between a high fly ball and a more looping strike. The idea with those systems are that over a large sample those variations balance each other out, but this new player tracking system will give teams and fans much more tangible evidence to determine if someone is a quality defender or not.

The biggest question will be how much of this data the MLB will hoard for themselves. PITCHF/x has been available in the public domain for years, so one can hope they will follow their own precedent (and the NBA’s) in releasing the data to the public. The possibilities for meaningful research are simply endless.

Third and Six

By Max Kaplan

League-wide, the conversion rate on 4th down is 50% but it jumps to 65% on 4th and 1. Ever wonder how likely it is to convert in a given situation? I ventured to find out, armed with the entire season’s worth of data from 2012.

The goal of 1st down is to create a manageable 2nd down. The goal of 2nd down is to create a manageable 3rd down. But you have to convert, eventually. What are the percentages of converting on a given down and distance?

1st down conversion by situation
*2012 Season
Note: Bigger rectangles are ranges of situations – used when
there weren’t enough plays in a given down and distance.

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Dishes and Dimes Part II – Passing Efficiency

by Aqeel Phillips

Halfway through the current NBA season, fans have celebrated and lamented the position of their teams as the contenders and lottery teams separate themselves from the pack. On the flip side, NBA stat geeks have begun universally celebrating as the SportVU player tracking system has filled up with an ample pool of data and now possesses a respectable sample size. More than 41 games into the season, we can not only start to project playoff seeding and start pondering matchups, but we can also begin to accept players’ performances so far as an expectation of how they will finish the season as well (barring injury or possible team-afflicting swaps at the trade deadline). SportVU allows us to take a deeper look at these performances, past the simple statlines of points, rebounds, and assists, and really get our hands dirty in finding out what might makes each team and player special.

A Revisit

To start, I’d like to revisit my previous article with a few revisions. A reader pointed out that the passing player’s free throws were not being subtracted from the team free throws, so players like LeBron James and Russell Westbrook benefitted from taking many free throws. In addition, it appears that Assist Percentage is a more helpful stat to use than Assist Rate for calculating free throws. The former is simply a percentage created by the amount of field goals assisted by a player out of the total team field goals made, while Assist Rate is a more involved metric that counts assists versus possessions in a game. Lastly, player minutes need to be factored in as well. Team points from free throws are tallied over the entire game, but a player is only on the court for a fraction of the game to assist on those free throws. As a result, we need to multiply the team free throws per game by the fraction of the game that a player is on the court.

Here is a comparison of my formula (specified in previous article) compared to the concrete data that SportVU provides this season, using this season’s data rather than the 2012-13 data I used previously.

Screenshot 2014-03-27 15.56.09

The formula has its flaws, specifically it has a tendency to overestimate the number of free throws catalyzed by a player’s passing. For example, the formula assumes that Chris Paul’s ridiculous 53.8% assist percentage also applies to the amount of free throws shots while he is on the floor. The formula projects him to catalyze 5.8 FTs per game, while reports that he only catalyzes 0.9 per game (almost the full difference between his projected points and his contributed points). Overall I believe it still gives a fairly good projection of how many points a player is contributing total. I think that it can still be a valuable tool for getting a picture of players’ contributions before SportVU was available.

(Note: AST+ is not available for this season, so I was forced to calculate it myself. A full explanation can be found after the conclusion of the article).

Introducing Passing Efficiency

SportVU has been tracking two pieces of player data never readily available before: Passes per Game and Points Created by Assist per Game (as mentioned previously). The points are a combination of passes leading to two-pointers, threes, free throws, and passes leading to assists (“Hockey assists”). To get a picture, here are the current top five in Passes per Game and Points Created by Assist per Game (which is desperately in need of a fancy acronym).

Screenshot 2014-03-27 16.12.02

Return to old format to affect length, not champion, of NBA Finals

by Gina Talt


The 2013-14 NBA season is officially underway, and more has changed than just the Brooklyn Nets and Dwight Howard’s jersey.  Last week the NBA owners voted unanimously to return to the 2-2-1-1-1 format of the pre-1985 Finals after 29 years of playing under the 2-3-2 format. This change will take place immediately, beginning with the 2014 Finals.

How will the new format affect future Finals series?

According to Commissioner David Stern, the format change reflected a sentiment among the teams in the league that the higher seed was not sufficiently rewarded for its better season record under the old 2-3-2 format. They believed it was unfair for the home team to have to play on the road for a crucial Game 5. Over the last 29 years, the higher seed has been down two games to three after Game 5 nine times. When you take a closer look however, this situation has been a recent trend of late. The higher seed in four of the previous eight Finals and three of the last four Finals has trailed the lower seed after Game 5. While the higher seed had home court advantage in Game 6 and 7, it needed to win both games to come out on top. Yet history shows that the lower seed had a slight advantage at this point. The lower seed ended up with the O’Brien trophy five out of the nine times when it held a one-game lead going into Game 6.

The data seem to back the NBA’s rationale for the change, but how likely was the same scenario under the new 2-2-1-1-1 format which was in place for 28 years before 1985? Continue reading