Sports Reference Blog

We've been listening to requests for new features from our users and we're happy to announce that we have launched a new tool for Stathead Football subscribers: The Team Split Finder

The Team Split Finder allows users to unleash the power of our splits database to run searches to compare how teams have fared in various situations. The splits are built off of our play-by-play data, which dates back to 1994.

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Nick Pazoles has joined Sports Reference as a DevOps engineer. Nick previously worked for Cleo in the Chicagoland area and is the first White Sox and University of Michigan football fan on staff.

On Basketball-Reference, we have a special page for Michael Jordan where we house his statistics from his high school and college career. Two other superstars that have been in the spotlight since high school are Kobe Bryant and LeBron James, and we have added special pages for them that display their statistics during high school.

Kobe Bryant's high school page allows you to follow his improvement over his years at Lower Merion, along with notes marking milestones such as his first 40-point game or his McDonalds All-American honoring. The page also includes Bryant's preseason games for the first half of his NBA career. We give special thanks to Todd Spehr for compiling and donating these records and notes.

LeBron James' high school page tracks his statistics at Saint Vincent-Saint Mary in Akron before entering the NBA, with full coverage of makes, attempts and rebounds.

If you have any suggestions or feedback, you can contact us via our feedback form.

Over the past few weeks, we have added a number of exciting improvements to FBref, many of which were requested by our users.

Pass Length Categories

We have adjusted the three categories for pass lengths.
Old Categories
Short: 0-5 yards
Medium: 5-25 yards
Long: 25+ yards

New Categories
Short: 5-15 yards
Medium: 15-30 yards
Long: 30+ yards

The issue previously was that the short category was including many passes that were blocked or deflected and therefore traveled fewer than 5 yards. This resulted in low completion totals for "short" passes, which is counterintuitive. To fix this, we are excluding passes traveling fewer than 5 yards in these categories. Now we see "short" passes as the most likely to be completed.

Defensive Actions included in Goal/Shot Creating Actions

We introduced the Shot and Goal Creating Actions metric earlier this year. This stat captures which events ultimately led to a shot or a goal. Previously, these actions were limited to the offensive side, such as Passes, Dribbles, and Fouls Drawn. We are now including defensive actions that lead to a shot or goal, such as Interceptions and Tackles.

Precise Ages on Current Season pages

For winter competitions, we list players' ages on August 1st of that season, to represent their age at the beginning of the competition. Likewise, we list ages on February 1st for Summer competitions. This works well for previous seasons, but it can be an issue for current seasons when players have a birthday during the competition. To fix this, we are now displaying precise ages on current season pages. These ages are in the YY-DDD format. For example, 27-123 indicates that the player is 27 years and 123 days old.

Ages on Match Reports

We have also added player ages to our match reports. These are the players' ages on the date of the match, listed in YY-DDD format.

Average Squad Age

Also regarding player ages, we have added each team's average age to the squad stats tables. These ages are weighted by minutes played.

Completed Matches

We have added Completed Matches to our Playing Time table. These are matches in which a player was in the starting XI and played the full match (eg was not subbed or sent off).

Average Shot Distance

Our Shooting tables offer a lot of detail about a player's shooting record. We have included the average distance from the goal of their shots. Note that this stat does not include penalty kicks and is listed in yards.

Opponent's Stats

Finally, we are now including opponent's stats on our squad stats tables. These are toggleable by clicking on the "Opponents Stats" buttons above each table. These stats show how opponents performed against a particular squad.

Opponent's stats are also included in squad pages, below player totals. This provides an easy way to compare a team's "For" and "Against" stats in each category.

I'm pleased to announce that Charlotte Eisenberg and Adam Darowski have joined Sports Reference in full-time roles.

Charlotte is joining us as a Data Developer working on baseball, basketball and hockey. She was an intern with Sports Reference previously and also spent a year with the Texas Rangers front office.

Adam is our new Head of User Experience. He has been a long-time consultant for Sports Reference and is responsible for the responsive redesign we undertook in 2016-17 and the design of our new Stathead service. You can follow him on twitter @baseballtwit.

In another move, Jaclyn Mahoney has been promoted from Data Developer and will now manage our Business Intelligence efforts.

About Sports Reference.

By popular demand, we're thrilled to announce that annual Stathead subscriptions are now available at Stathead.com. Stathead is the premier set of sports research tools available to the public and is available for Baseball, Basketball, Football & Hockey. Monthly subscriptions, which are $8/month for a single sport and $16/month for all sports also remain available. The new annual subscriptions are priced to give users who choose them two months for free: $80/year for a single sport or $160/year for all sports. If you're a current subscriber and wish to change from monthly to annual, you may do so here. To learn more about Stathead, please visit here.

In addition to covering the NBA and WNBA in the United States, Basketball Reference covers an array of professional basketball leagues in Europe, Asia and Australia. For the 2020-21 season, we have added four new leagues: the ABA Adriatic League, the VTB United League of Russia, the Turkish Super League and the Israeli Super League. These leagues include teams that participate in the continental EuroLeague and EuroCup competitions, which we cover as well.

Notable former NBA players who are playing in these newly added leagues include Sam Dekker and Tyler Ennis with Türk Telekom in Turkey, Eric Mika with Partizan NIS in the Adriatic League, Greg Monroe and Jonas Jerebko with BC Khimki in VTB United, and Dragan Bender with Maccabi Tel Aviv in Israel.

For these newly added leagues we only have current season data right now, but if you'd like to see previous seasons for those leagues or express interest in other leagues, feel free to contact us via our feedback form.

Last month, we added Championship Leverage Index (cLI) and Championship Win Probability Added (cWPA) to Baseball-Reference. These stats measure how much of an impact each player had on their team's chances of winning the World Series. Today, we are launching the Pivotal Play Finder, which measures the impact that each individual play had on a team's World Series win probability. This tool allows you to customize your query using a number of different filters to find the most impactful plays in a given situation.

It's not surprising to see that the most pivotal play in MLB history occurred in Game 7 of the 1960 World Series. But many would be shocked to find out that it was not Bill Mazeroski's walk-off (which is 6th all-time). The most pivotal play actually occurred an inning earlier. In the bottom of the 8th inning with 2 outs, the Pirates were down 7-6 with runners on the corners when Hal Smith put his team up by two runs with a 3-run home run. This play increased the Pirates' chances of winning the World Series from 30% to 93%. Unfortunately for Smith, the Yankees erased the lead in the top of the 9th, and then Mazeroski became the hero.

With the Pivotal Play Finder, you can search by event type and find out that Babe Ruth's caught stealing to end the 1926 World Series was the most impactful caught stealing in MLB history (10.22%), or that Fred Snodgrass' muff in the 1912 World Series was the most critical error in history (24.39%).

We can also search for plays involving a particular player. Derek Jeter was involved in many memorable moments during his career, but none more pivotal than his walk-off home run in Game 4 of the 2001 World Series.

We can drill down even further and search by team to see that Randy Arozarena's home run off Lance McCullers Jr. in Game 7 of the 2020 ALCS was the most pivotal home run in Tampa Bay Rays history.

In addition to sorting by Championship Win Probability Added, we can also sort by Championship Leverage Index to find the most crucial moments. These situations are usually the most pressure-packed because the difference between an out and a run has an enormous impact on a team's World Series win probability. The situation with the highest cLI in MLB history came in Game 7 of the 1962 World Series. In the bottom of the 9th inning, the Giants were down 1-0 with 2 outs and runners on 2nd and 3rd with Willie McCovey at the plate. A hit would likely tie or win the game (and World Series) for the Giants, while an out would mean a championship for the Yankees. As we know, McCovey lined out sharply to Bobby Richardson to end the series.

Please note that at the time of this writing, Regular Season event data is complete back to 1973, mostly complete back to 1950, and somewhat complete back to 1916. Postseason event data is complete back to 1903. Please see the data coverage page for details.

Just in time for the 2020 postseason, Baseball-Reference has added championship win probability added (cWPA) and championship leverage index (cLI) to the site.

Just as single-game win probability added (WPA) measures how a player impacts their team's chances of winning a game, cWPA measures how a player impacts their team's chances of winning the World Series. Similarly, championship leverage index uses the same concept of single-game leverage index (LI), but expands the scope to measure the importance of a particular play, in how it impacts a team's chances of winning the world series.

These stats are highly dependent on context and are best used as "story stats" rather than determining which player was better. When telling the story of the history of baseball, we point to the greatest moments such as Bobby Thomson's Shot Heard 'Round the World, Bucky Dent's home run over the monster, David Freese's clutch performance in Game 6, or Madison Bumgarner's Game 7 performance. Moments like these are captured in cWPA and cLI, but it's not just history's greatest moments. Every event in our play-by-play database has a value.

How are cWPA and cLI calculated?
Let's look at Bobby Thomson's Shot Heard 'Round the World for example. This was the third and final game of the National League tiebreaker series. A win for the Giants would clinch the pennant with a 50% chance of winning the world series. However, a loss would end their season, meaning a 0% chance of winning the world series. The difference between a win and a loss in this game is 50%. To get the championship leverage index, we simply divide .5 by our baseline of .006 (The baseline is explained here). This means that the Giants' cLI for the game is 83.33 (.5/.006). The LI for Bobby Thomson's final at-bat was 4.74. To get the cLI for the at-bat, we simply multiply the game cLI by the at-bat's LI, which gives us 395.0 (83.33*4.74). This mean's that this at-bat is 395x more important to the Giants' chances of winning the world series than the average play on opening day.

When Thomson stepped to the plate, the Giants were down 3-1 with 1 out in the bottom of the 9th, giving them just a 29% probability of winning the game at the start of the at-bat. Since the home run ended the game, the probability of winning the game at the end of the at-bat was 100%. To get the cWPA for the play, we multiply the difference between game win probability at the start and end of the at-bat by the difference between the championship win probability of a win and a loss. This gives Thomson .355 cWPA ((1.0 - .29) * (.5-0)). This means that Thomson's home run increased the Giants' probability of winning the world series by 35.5 percentage points. On the flip side, the opposing pitcher Ralph Branca is given -.355 cWPA for the play.

Note: cWPA values are displayed in percentage format, so the example above displays as 35.5%.

There are currently a number of places to find cWPA and cLI on Baseball-Reference:
Regular Season Leaderboards: Career Regular Season Batting Leaders
Postseason Leaderboards: All-Time Batting Leaders
Batting and Pitching Game Logs: Yaz's amazing 1967 season
Batting and Pitching Win Probability Tables: Sandy Koufax's Pitching Win Probability
Postseason Series Pages: 1991 World Series
Box Scores: 1960 World Series Game 7
League Batting and Pitching Win Probabiliy Pages: 2020 MLB Batting cWPA
Team Batting and Pitching Win Probability Tables: 1975 Reds Batting
Team Schedules: 1978 Yankees

If you have any questions or feedback on this new feature, feel free to contact us through our feedback form.

In previous months, we have launched Stathead Baseball, Stathead Hockey and Stathead Basketball. Today, we're thrilled to announce that Stathead Football has joined our suite of Stathead tools, the most powerful set of sports research tools available to the public. In fact, they're so good that the pros (whether they're running teams or covering them) also swear by them.

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