At yesterday’s opening keynote at SAPPHIRE NOW, SAP co-CEO Bill McDermott talked about how SAP is working with professionals sports leagues, teams, and companies to create the next-generation fan experience. I’ve been meaning to write this blog post for a while, and Bill’s keynote – in addition to the fascinating panel discussion including Adam Silver, the next Commissioner of the NBA – inspired me to finally sit down and write this blog. So, this is the latest in my series of sports and entertainment-related blog posts to help fans understand the value SAP will be bringing to our 25th and newest vertical industry market

NBA.com/stats

As a lifelong basketball fan, I was thrilled when the NBA launched its new NBA.com/stats website powered by SAP HANA. Not only does the site provide access to every NBA box score since 1946(!), but more importantly it unleashes a treasure trove of advanced metrics for fans to understand and appreciate the game at an entirely new level.

When I checked out the site, I realized that the sheer volume of data might be a bit overwhelming for the casual fan. There is a nice glossary of all the new stats, but I thought it would be useful to have some “snack-sized” examples to help fans understand all this data in a specific context. With that in mind, my goal is to help hoops fans understand some of the countless new insights to be found on NBA.com/stats.

Five “New” NBA Statistics You Should Know

Since we’re in the midst of the NBA playoffs, this post will be a “Sabermetrics 101” if you will on 5 relatively new basketball metrics that offer a more accurate way to look at individual players and teams. I hope you find it useful!

Stat #1: Effective Field Goal Percentage (eFG%)

• Comparable “traditional” stat: Field Goal Percentage
• Explanation of eFG%: Measures field goal percentage adjusting for the fact that a 3-point field goal is worth one more point than a 2-point field goal. Calculated as: ((FGM + (0.5 * 3PM)) / FGA
• Why it’s better than the traditional stat: Traditional Field Goal Percentage assumes that every shot is worth the same, but of course we know that 3-pointers are worth than 2-pointers, so this new calculation simply weights the point values accordingly.
• An example of why eFG% is better: The 2012-2013 New York Knicks shot 44.8% from the field overall, which ranked just 17th in the NBA. However, after considering the increased value of 3-point shots, they moved up all the way to 7th with an eFG% of 51.5%, a clear indication of the impact of their long-range shooting. Of course, there are trade offs to “living and dying” with 3-point shots, which you will see later.

Stat #2: Defensive Rating (DefRtg)

• Comparable “traditional” stat: Points Allowed per Game
• Explanation of DefRtg: Measures a team’s points allowed per 100 possessions.
• Why it’s better than the traditional stat: A team that gives up a lot of points per game may seem to have a poor defense, but it could be a function of their style of offense. For example, if a team has a fast-paced offense and they take shots every 5-10 seconds, this will conversely allow the other team to have more opportunities to score on them, and therefore allow more points on a per game basis. So instead of using Points Allowed per Game, we look at the Points Allowed per 100 Possessions to adjust for the pace of play.
• An example of why DefRtg is better: During the regular season, the San Antonio Spurs ranked 11th in points allowed giving up 96.6 points per game. However, the Spurs rank 3rd overall in Defensive Rating giving up 99.2 points per 100 possessions. This is because San Antonio plays a relatively fast paced style of offense (which is surprising given their veteran roster). They also play in the Western Conference which has 9 of the 10 fasted paced offenses in the NBA. So not only do they shoot at a relatively fast pace, but their opponents play a similar tempo which yields more points on a per game basis. After adjusting for the pace of play, you realize that their defensive efficiency is outstanding. When you combine this with the fact that San Antonio’s offensive efficiency (points scored per 100 possessions) is also in the Top 10, you can see how the Spurs finished with a 58-24 regular season record, 3rd best in the NBA.

Stat #3: Offensive Rebound Percentage (OREB%)

• Comparable “traditional” stat: Offensive Rebounds per Game
• Explanation of OREB%: The percentage of available offensive rebounds a player or team grabbed while on the floor.
• Why it’s better than the traditional stat: Rebounds per Game is misleading in that it is entirely dependent on the number of shots taken and missed. To use an extreme example, if a team makes every one of its shots in a game, it would have 0 offensive rebounds. Does that mean they are poor offensive rebounding team? Of course not, which is why the Rebounds per Game stat is problematic. It’s more useful to measure the percentage of rebounds collected against available rebounds, aka Offensive Rebound Percentage.
• An example of why OREB% is better: By traditional measures, the Milwaukee Bucks had a great offensive rebounding team, ranking 2nd in the NBA with 13.0 offensive boards per game. But that was largely due to the fact that they took (and missed) so many shots, giving them plenty of opportunities to grab offensive rebounds. If you look at Offensive Rebound Percentage, they fall all the way to 11th at 27.9% OREB%. This allows you to compare across teams more accurately by removing the inherent advantage given to high volume + poor shooting squads.

Stat #4: Percentage of Field Goals Assisted: FGM (%AST)

• Explanation of FGM (%AST): The percentage of a player or team’s field goals that were assisted from a teammate.
• Why it’s a valuable stat: As the name implies, % of Field Goals Assisted reflects the percentage of made shots as a result of an assist from a teammate. This stat is interesting in that is shows how good players or teams are at creating their own shots vs. relying on teammates to facilitate scoring opportunities for them. On the flip side, if you’ve been following the NBA playoffs this year, you know that being good at creating your own shot (and not involving your teammates) isn’t always a positive thing.
• An example of FGM (%AST): There are two big stories in the NBA playoffs now which can be traced back to this stat:
• The team with the lowest % of Field Goals Assisted is again the New York Knicks. This makes sense because their offense is geared towards isolating their two best scorers (Carmelo Anthony and J.R. Smith, both outside shooters) at the expense of “spreading the wealth” on offense. The Knicks had a great regular season but are about to be eliminated from the playoffs as Anthony and Smith have not been shooting well. A mini-crisis has emerged as teammates are criticizing the offense’s effectiveness (quote from Tyson Chandler: “We need to do a better job of allowing the game to dictate who takes the shots and not the individuals.” Unfortunately, the Kicks’ offense and roster is not built to distribute the scoring load, so when their two big scorers go cold, their reliance on 1-on-1 isolation plays get fully exposed.
• Similarly, the team with the 3rd lowest % of Field Goals Assisted (Oklahoma City Thunder) is also on the verge of being eliminated after losing All-Star point guard Russell Westbrook to injury. With no real offensive system and just one star player (Kevin Durant) who is expected to do most of the scoring, Durant is sometimes even getting quadruple-teamed (see video below).

Stat #5: Player Impact Estimate (PIE)

• Explanation of PIE: PIE is an all-encompassing stat that measures a player’s overall statistical contribution – ie, what % of game events did that player achieve. An “event” represents stats such as points, assists, and rebounds, as well as personal fouls, turnovers, and defense. A high PIE is highly correlated to winning, and a PIE of >10% means you are a better than average player.
• Why it’s a valuable stat: There are other “all-in-one” stats such as Player Efficiency Rating (PER) which serve the same purpose but are calculated differently. The NBA has chosen PIE but there’s no real consensus as to which stat is “best”. The most important thing however is to choose one metric and use that as a consistent basis for comparison.
• PIE Rankings: 2012-2013 regular season:

It’s interesting to see that Tim Duncan and Tony Parker are in the Top 5 of all NBA players. They don’t have the most eye-popping traditional stats, but their overall contributions (and lack of weaknesses) all add up to their overall value. And they are the two biggest reasons why the Spurs had the 3rd best record in the NBA.

But hey, let’s get real. The #1 rated player here is no surprise. Any stat will tell you that LeBron James is on a different planet from all the other basketball players, so let’s just wrap up this list with a gratuitous highlight reel of King James’ Top 10 Plays from this season. Enjoy!

The stats I just described are a small fraction of the information available at SAP.com/stats. I plan to write more blogs utilizing other features of the site including shot charts and historical game logs from the past 67 years. In the meantime, check it out yourself and play around with the data. Feel free to comment below to share your opinions of the site…or just to talk NBA basketball!

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1. Christopher Kim Post author

Thanks Manoliu! Sabermetrics in soccer is still just getting started because it’s a more challenging sport to quantify. As you know, soccer is a very fluid sport as opposed to baseball or football which have a lot of discrete “events” to quantify. Still, there is a big opportunity here because the traditional soccer stats (goals, assists, number of shots) are clearly not sufficient to measure or predict a player’s contribution. We are starting to see better stats as well as interesting heat maps which can isolate which players are adept at initiating scoring opportunities for their team (eg, Xavi at Barcelona).

This article gives a great summary of where soccer is currently at, with progress continuing to be made: http://www.ft.com/intl/cms/s/2/9471db52-97bb-11e0-9c37-00144feab49a.html#axzz1PjVXJPdI

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1. Richard Hirsch

Interesting blog – especially the new metrics.  It is another example of the increasing importance of algorithms in HANA-related projects. Maybe SAP should create (and sell!) a sports-related package for HANA  with all these statistics.

D.

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1. Christopher Kim Post author

Thanks Richard.

This blog is really just the tip of the tip of the iceberg. The next major advancement in sports is using video tracking technology to track each player’s movements and using spatial analysis to quantify their impact on the game.

The seminal research on this was a recent study to identify the best defensive player in the NBA. It’s an amazing paper, using visual tracking technology from SportVU to quantify a player’s impact on defense much more accurately than traditional stats like blocked shots and steals. If you have time I recommend reading this research paper by Kirk Goldsberry, a visiting scholar at Harvard and my personal hero for NBA analytics.

Perhaps SAP could partner with a company like STATS (which owns the SportVU technology) and run all of their data-based products on HANA?

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1. Richard Hirsch

Thanks for link to the article – it will provide excellent reading material for my trip home. I glanced at the article and found its diagrams describing various metrics intriguing.  I think that may be the toughest part of such work – how do you visualize such stats that people can understand them.

D.

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2. Pascal Matt

WOW…These hole new 25th industry market Sports&Entertainment is pretty amazing and congrats to this wonderfull blog.

For me as a passionate athlete it could be a perfect Topic for my Bachelor Thesis ðŸ™‚

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