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Evaluating Team Shot Selection

US Presswire

US Presswire

A few weeks ago I shared a new metric I had been working on called Expected Points Per Shot (XPPS). The idea was to create a metric that would allow the objective comparison and evaluation of the quality of a player or team’s shot selection. When I first shared XPPS we just looked at the numbers for individual players. The visualization with player data is now a permanent feature here, one that I’ll keep updated throughout the season. Today I have a new Tableau visualization, with team numbers stretching back to the 2000-2001 season. Below there is an explanation of XPPS, in case you missed that first post. If you are already familiar with the stat, feel free to skip ahead to the goodies and the analysis below.

The foundational piece of Expected Points Per Shot is the understanding that not all shots are created equal. A layup is much more likely to go in than a long jump shot. A three-pointer is also less likely to go in than a layup, but if it does go in, it earns an extra point. All these trade-offs can be measured numerically. I used statistics from and looked at every shot, made and missed, going back to the 2000-2001 season. The NBA groups those shots into five locations – Restricted Area, In The Paint (Non-RA), Mid-Range, Corner 3, Above The Break 3. By calculating the total number of points scored on shots from each location and dividing it by the number of attempts we arrive at an expected value for shots from each location. Here are those averages:

  • Restricted Area –  1.183
  • In The Paint (Non-RA) – 0.793
  • Mid-Range – 0.788
  • Corner 3 – 1.157
  • Above The Break 3 – 1.048

For my evaluation I also included free throws. The basketball stats community has agreed on 0.44 as the standard modifier for calculating shooting fouls from total free throw attempts. That means that multiplying 0.44 by a player or team’s  total free throw attempts will give you a very close approximation of the number of times they went to the free throw line for two shots. I also calculated the average value of a trip to the free throw line for two shots as 1.511.

With those expected values we can calculate a player or team’s Expected Points Per Shot. We multiple their total attempts from each area by the expected value of shots from that area. We add that total to the totals from all other areas. We then divide that total by all of a player or team’s shot attempts, including the calculated trips to the free throw line. The result is Expected Points Per Shot.

It’s important to remember that this is a measure of the quality of a player’s shot selection. Players who take a lot of easy shots like layups or corner three-pointers will have a higher value. However, players under and over-perform league averages all the time. For that reason I compare Expected Points Per Shot to Actual Points Per Shot. Calculating the difference between the two lets us see who’s shooting accuracy is better or worse than we would expect.

Before we get to the visualization, there are few things to remember. The central graph marks each team, going back to the 2000-2001 season by both their Actual Points Per Shot and their Expected Points Per Shot.  The shape of each mark indicates the season it came from. The color of each mark represents that team’s Offensive Rating (points scored per 100 possessions).

Below the central graph are set of filters. This will let you control the display of the graph. You can filter by team and season, or narrow the results by ORtg., XPPS, Actual Points Per Shot, or the difference between the two. If at any point you want to reset all the filters, click the button at the bottom of the page that looks like a circle with an arrow. I need to do some adjustments to my data so that the visualization can build a summary table at the bottom. At this point the only way to view the actual numbers is to scroll over the mark for a team. A dialog box should pop up identifying the team and showing their actual numbers.

Below is the visualization along with few things I noticed from this first look-through.



In terms of shot selection there are a few historic abnormalities occurring so far this season. At this point, the 2012-2013 Houston Rockets have the highest XPPS of any team over the past 13 seasons. The highest value shots come either at the rim or from behind the three-point line, and altogether 72.1% of Houston’s field goal attempts this season have come from one of those two locations. No other team in the league is above 66%. If you factor in trips to the line for two free throws, which is the highest value option, 76% of the Rockets’ scoring opportunities have been used on the highest value opportunities.

It’s easy to attribute this historic attention to efficiency to the addition of James Harden, but his individual XPPS is 1.055, just a hair above average. The Rockets with the most efficient shot selection actually play in the frontcourt – Greg Smith, Marcus Morris and Omer Asik. Offense from Smith and Asik comes almost exclusively at the basket and from the free-throw line. Morris fills the stretch-forward role, but does it in a very efficient manner. Just 14.4% of his FGA have been mid-range jumpers this season. However, he has shown legitimate three-point range which means he can provide spacing from the power forward position without having to take low value shots. 45.2% of his field goal attempts this season have been three pointers, with 25.2% being those magical corner threes.

Efficiency is also extremely balanced on the Rockets. Of the nine players who have played at least 300 minutes on the season, averaging at least 10.0 per game, only three – Chandler Parsons, Carlos Delfino and Patrick Patterson have a below average XPPS. All three rely on mid-range jumpers, generally an ineffective option, but all three are respectable shooters from that distance. Patterson and Delfino are both right around 39% on the season, and Parsons is shooting an eye-popping 56.8%.

Oklahoma City, Miami and San Antonio

Ultimately shot-selection matters because it’s about finding shots that are more likely to go in. XPPS is based on league averages, but the nature of averages means teams may find themselves way above and way below that mark. This season there is a historic above-average clumping. At this point in the season, the 2012-2013 Heat, Thunder and Spurs have the three highest actual points per shot marks of any team over the past 13 seasons. All three of have XPPS marks that are well above average but they have out-performed their expected totals by 0.107, 0.112 and 0.090 points per shot, respectively. Those numbers may seem small on a per-shot basis, but per 100 shots we are looking at a difference of between 9 and 11 points better than average.

For Miami this result is a combination of the great individual efforts of LeBron James, Dwyane Wade and Chris Bosh, and the effect that has on their three point shooters. I pointed this out a few weeks ago, but all three players have relatively inefficient shot-selections, relying heavily on mid-range jumpers. However, all three are remarkably efficient on those shots, out-performing their XPPS by 0.171, 0.098 and 0.088 points per shot, respectively. The fact that LeBron, Wade and Bosh can swallow up so many of the unavoidable inefficient possessions that every team creates, and do it in an efficient manner, takes an incredible amount of pressure off their shooters. Just look at the way Shane Battier (+0.067), Ray Allen (+0.196), Rashard Lewis (+0.197) and Mike Miller (+0.087) have out-performed their XPPS.

Although it doesn’t connect quite as explicitly to outside shooting, we find roughly the same pattern in Oklahoma City. Russell Westbrook is slightly under-performing his shot selection, but Kevin Durant (+0.235) and Kevin Martin (+0.207) are both over-performing by huge margins. Again, this makes things easier for players in the supporting cast to out-perform their own shot selections. The chief beneficiaries are Nick Collison (+0.200), Hasheem Thabeet (+0.056), Thabo Sefolosha (+0.090) and Serge Ibaka (+0.198).

San Antonio has the highest XPPS of these three teams, and their system justs augment the pattern we saw above of the efficiency of high-usage players making things easier for their teammates. Tim Duncan (+0.117), Tony Parker (+0.123) and Manu Ginobili (+0.052) are all out-performing their shot selection by healthy margins. The amazing things is that almost every one of their teammates are as well. On their roster, only DeJuan Blair (-0.081) and Stephen Jackson (-0.153) are under-performing their shot-selection.


The hapless Wizards stand out for a completely different set of reasons from the teams I mentioned above. With an XPPS of 1.008 they have this season’s worst shot-selection. With an Actual Points Per Shot of 0.973 they are also this season’s least accurate shooting team. Their XPPS is 0.038 below the league average, and they are under-performing their XPPS by an additional 0.037 points per shot. There are plenty of places to point fingers, including at the front office who assembled this mess, but I’d like to point one each at Jordan Crawford and Kevin Seraphin.

Seraphin has the lowest XPPS of all players who have played at least 700 minutes with a Usage Rate above 22.0%. Crawford has a reputation as an incredible shot-maker, and it’s true. Watch him for even a single quarter and you’ll catch some made-shots with a breathtaking level of difficulty. The problem is that the level of difficulty is more consistent than the shot-making. Crawford outperforms his shot selection by 0.046. Unfortunately his XPPS is 0.990, 0.059 points per shot below the league average. If he was able to transfer that same shot-making ability to a league average shot selection he would average 1.093 points per shot, roughly the same as Kevin Garnett has this season. Shot selection matters.

This team visualization will also be updated regularly throughout the season and has a permanent home under the Statistics and Visualizations Tab in the top-right hand corner of our homepage.

  • brgulker

    Awesome awesome stuff.

    • Ian Levy

      Thanks Ben, I really appreciate that. I’m hoping to keep the team numbers updated all year, so we’ll be able to watch them change.

  • David Berri

    Excellent analysis. One would hope every NBA team is paying attention to this.

    • Ian Levy

      Thanks Dave!

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