The NBA trade deadline is tomorrow, and that means it's a good time to look up what your team's General Manager has been up to recently. To that end, you can consult the BBR Executives Database, wherein we list the player-personnel decision-maker for every team-season in NBA & ABA history, complete with their career transactions. For example (and a good laugh), here are the moves Isiah Thomas made in his distinguished career as an executive:
At the beginning of the 2011-12 NCAA Basketball season, we began to calculate Adjusted Offensive and Defensive Ratings for each team. In a nutshell, these ratings measure a team's offensive and defensive abilities independent of the pace at which they play. Since raw PPG totals can be greatly influenced by how many possessions a team has to score and defend, per-possession metrics more accurately reflect a team's true skill levels (pace is largely a choice, and it has almost no correlation to how good a team is at scoring and defending on a per-possession basis).
Here were the best teams during the regular season by adjusted net rating (adjusted Offensive Rating minus adjusted Defensive Rating):
This column was supposed to appear in the New York Times last week, but due to an editorial snafu it was never published. However, the piece was so good (ha-ha) that they asked me to update it and it was published in today's paper:
First, we now have full box scores dating back to the 1986 playoffs, which means we have complete coverage for the regular season and playoffs from the 1985-86 season to the present.
Second, you can now select rows to sum on the player pages. For example, go to Ray Allen's page. Suppose you want to see Allen's cumulative statistics for the Celtics. To do this, click on the row for the 2007-08 season (the row should now be highlighted in blue), then click on the row for the 2011-12 season. A pop-up will appear with Allen's cumulative statistics for these seasons.
Searching for players can be a chore sometimes. You want Alex Rodriguez's page, so you search for Rodriguez and get like 2,000 results, which we need to do, so you can find every player. But 99% of the time you are probably looking for A-Rod and not the other players.
Adding the string "the" to the start of a search will automatically send you to the most popular player page matching that search. Also, you don't have to enter the entire player name just entering the first few characters will work as well. We've always has the "the" feature, but I just tweaked it so that it now matches only on first and last names and no longer on things like given names or nicknames. So search "the L" now sends you to Cliff Lee instead of Barry Lamar Bonds. Enjoy.
The data isn't 100% complete, but thanks to our relationship with the Society for American Baseball Research, we've added a substantial amount of information about players who were out of organized baseball for a year or more. This will show up on the major league batting and pitching tables for the affected players. Around 1100 player pages are affected by this change. Please let us know about any errors or omissions you find and we'll fill in as many of the missing details as we can.
Watching this video made me nostalgic for vintage, early-2000s Vince Carter:
That's why I'm going to use Carter's finest season, 2001 (when he posted a career-high 25.0 PER, 12.9 Win Shares, and +6.3 SPM), as my example to take you through the PI+ Shooting Splits & Shot Charts. Read the rest of this entry
Today we're rolling out a new feature -- expected points on boxscores, year pages, and team pages back through 2000. What are expected points, you ask? Inspired by a concept introduced by Bob Carroll, Pete Palmer, & John Thorn in their 1988 book The Hidden Game of Football, our crack staff of statisticians (Sean and Neil) have used a decade's worth of play-by-play data to develop a formula that estimates the expected number of points given a combination of down, distance, and yard line. Let's look at this year's Super Bowl for an example. The first play after the kickoff looks like this:
That means that given a first and ten from their own 23, the expected value of the next score in the game is roughly 0.48 points by the Giants. With a run for no gain (resulting in second and ten from their own 23), the play changed that to -0.07 expected points, meaning that the play itself had a value of -0.56 expected points. Looking at the first touchdown of the game, we see:
Before the play was run, the expected points for a second and goal play from the 2 yard line is 5.72 points, so scoring the touchdown there netted the Giants 1.28 expected points (note that for touchdowns, 7 points are assumed and a made extra point is not worth any additional points, though a missed extra point subtracts one and a made two-point conversion adds one).
What can we do with these? Well, when you sum all of the differences together (minus an adjustment for the end of half and end of game plays), you should get the scoring margin of the game, which you can see in the table below the box score:
Given this table, we can break down the contributions each team's various squads made to the margin of victory -- the Giants' passing offense was their largest positive contributor, while their rushing offense actually contributed negatively overall. In addition to the box score pages, team totals have been added to team and year pages so you can see what cumulative totals each teams' offensive and defensive squads have.
Keep an eye out for further features using this play-by-play data as the year progresses.