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August 2021 Park Factor Update

31st August 2021

Today we released an update to how we calculate one-year park factors for 2020 and three-year park factors that include 2020. In short, we are giving the observed effects of ballparks in 2020 less weight, impacting context-adjusted stats like ERA+, OPS+, Rbat+, and WAR for 2019 through 2021.

There are two reasons for this change. First, the shortened 60-game season decreases the sample of games we have data from, which naturally reduces the significance of the data collected. Second, since teams only played within their own divisions in 2020, comparing scoring in home games vs. scoring in away games does not tell an accurate story of how a park impacted scoring relative to league average, since most of the parks in the league are not included in either set of games (e.g. when computing the park factor for Wrigley Field, games played at Coors Field or Citizens Bank Park are not included in the calculation anywhere, since the Cubs did not play away games at those parks in 2020).

The issue with the way we had been handling 2020 park factors became more apparent as the 2021 season went on, particularly because the Cincinnati Reds’ 2020 park factor of 119 was raising the 2021 three-year park factor, resulting in worse-than-expected adjusted stats for hitters like Joey Votto, and better-than-expected adjusted stats for pitchers like Wade Miley.

Now, when you look at a 2020 team page, the one-year park factors have been diluted so that they include an average of 60 games’ worth of 2020 data, and 51 games each of 2019 and 2021 data. If there is no corresponding 2019 or 2021 data (e.g. new ballpark in Texas, different mix of parks for Toronto), those parts are replaced with a league-average park factor of 100. These new one-year park factors are used in the three-year averages like usual, so the effect is reflected there as well.

With this change, here are some of the most notable movers in Wins Above Replacement:

Zack Wheeler (+0.5) and Aaron Nola (+0.3) each saw a bump up in their 2021 pitching WAR as the 3-year park factor for Philadelphia rose from 96 to 98 (frequent opponent Washington also had their 3-year park factor increase from 93 to 96). Wheeler’s 0.5 is the largest change resulting from this update.

Wade Miley, Tyler Mahle, and Luis Castillo (-0.4 each) saw their 2021 pitching WAR fall. As mentioned above, Cincinnati saw some of the most anomalous park factors in 2020, and mitigating their impact here lowers the expected run environment for these and other Reds pitchers.

On the hitting side, the changes are more modest. Justin Upton and Isiah Kiner-Falefa each saw their 2021 batting WAR rise by 0.3, while Maikel Franco and J.T. Realmuto lost -0.3 from their 2021 WAR.

In 2021, Shohei Ohtani is notably untouched by this update, with changes to his batting WAR and pitching WAR canceling each other out and his 7.9 total WAR remaining the same.

Here is a full list of changes to park factors, rate stats, and Wins Above Replacement from before and after this change.

Posted in Advanced Stats, Announcement, Baseball-Reference.com, Statgeekery, WAR | Comments Off on August 2021 Park Factor Update

2021 WAR Update

31st March 2021

As we approach the beginning of the 2021 season, we have made some updates to our Wins Above Replacement calculations. You may notice some small changes to figures as you browse the site. As always, you can find full details on how we calculate WAR here.

Defensive Runs Saved Changes

Last week, we updated Defensive Runs Saved (DRS) totals across the site with new figures from Sports Info Solutions that incorporate more accurate hit timing data. This impacts some fielders from 2017 to 2020. You can read more about the updates in the Sports Info Solutions blog, including which teams and fielders were most impacted.

2019 Park Factors

Park factors for 2019 have been re-computed to include the 2020 season, since WAR uses a three-year average for park factors when computing pitching WAR. The most significant change here is the Cincinnati Reds, whose pitching park factor rose from 103 to 108 (where <100 represents a pitcher’s park and >100 represents a hitter’s park). Luis Castillo sees the biggest benefit from this, with his 2019 WAR rising by 0.7 wins. All other changes to pitching WAR from updated park factors are smaller than Castillo’s 0.7 WAR gain in 2018.

2020 Park Factors

When a season is in progress, our three-year average park factors are computed using a prorated combination of the current season and two years prior. Due to the shortened 2020 schedule, the park factors for 2020 were still using some data from 2018, because the 60-game schedule was being treated as a partial in-progress season. We’ve addressed this in our park factor calculations so that the 2020 park factors only include 2019 and 2020. This change was reflected in OPS+, ERA+, Rbat+, and rOBA in the past week, but it is now also incorporated in WAR, leading to small changes for a handful of players.

Lance Lynn gains the most from this, adding 0.3 wins with Globe Life Field moving from a slight hitters park (102) to a more extreme hitters park (107). Trea Turner has the largest change on offense, also gaining 0.3 wins with Nationals Park moving from being a slight hitters park (102) to being a slight pitchers park (98).

New Game Logs from Retrosheet (1901-1903)

Last summer, we updated the site with new data from Retrosheet, including new game logs for players from 1901 to 1903. Having game-level data allows us to be more precise in our WAR calculations, since we can consider the specific ballparks a pitcher played in and the opponents he faced.

We presented a more in-depth example of this in our last WAR update, when Hall-of-Famer Christy Mathewson’s WAR rose after we added new game logs. This time around, pitcher Doc White saw the biggest change, gaining 1.5 WAR over the course of his career.

Biggest Career Movers

The top mover for position players in career WAR is Trea Turner, gaining 1.8 wins through a combination of additional runs saved and beneficial park factor changes. Trevor Story is close behind at 1.7 wins, primarily through additional runs saved.

On the pitching side, we see Doc White with 1.5 wins gained as described above. Among modern players, Patrick Corbin saw his career total drop by 0.8 wins. This is the flipside to how Turner gained credit. Corbin is debited for playing in a more pitcher-friendly park than previously thought, and for playing in front of defenders like Turner who are getting additional credit for their defense. Both of these changes decrease the number of runs we’d expect Corbin to have allowed, and as a result his performance is not as valuable as previously calculated.

We’ve highlighted some of the more extreme changes here, but to see full lists of the largest changes to season and career WAR totals, please see the spreadsheet here.

Thanks to Baseball Info Solutions and Retrosheet for their contributions. Please let us know if you have any comments, questions or concerns.

Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Features, History, Statgeekery, Stathead, WAR | Comments Off on 2021 WAR Update

Advanced Stats on Player Pages: How We Made It

26th February 2021

On Tuesday night, we added a new table of Advanced Stats to player pages.

This is what it looks like for hitters:

Mike Trout Advanced Stats

And for pitchers:

Gerrit Cole Advanced Stats

Rather than simply explain what we added, I’m going to describe how we added it. How does something go from an idea to a feature on Baseball-Reference? The entire process starts with you, the user.

At the beginning of January, we began conducting interviews with several users to discuss their experience using Baseball Reference and Stathead. By the time we launched the feature, we had spoken with nearly 50 users. It’s important to note that when we started the interview process, we didn’t have a particular solution or even a particular problem in mind.

There were several goals for these interviews. We wanted to find:

  1. What is the general perception of Baseball Reference compared with other sites?
  2. What features would users like us to add to Baseball Reference?
  3. What features would users like us to add to Stathead?
  4. What features of Baseball Reference and Stathead are users having a hard time using, finding, or just remembering to find?
  5. In what ways are people using our sites that we hadn’t anticipated?

Many of the interviews confirmed what we already knew. But every interview had at least one piece of gold that we could learn from. One interview in particular stood out to me and sent me on a path towards designing the feature you see on the site today.

I spoke with Mark Gorosh (@sportz5176 on Twitter) on February 3. Mark was lamenting that we don’t have advanced metrics such as BB% and K% on Baseball-Reference player pages. He didn’t understand why we had so many columns about the inner workings of WAR (in the Player Value table), but not established advanced stats like walk rate.

The issue, of course, is that we do have those stats. At this point I showed Mark the Advanced Batting page and… I’m not going to say Mark yelled at me, but he gave us some tough love that we really needed to hear. He couldn’t understand why all these great stats were not on a player’s main page.

And he was right.

There were a few different paths we could take.

  1. We could take all of the tables on the Advanced Batting pages and put them on the main player page. This wasn’t practical, however. There’s also an Advanced Fielding page and, of course, and Advanced Pitching page for pitchers. Adding all of these for a pitcher would lead to dozens of tables. Having so much on one page would negatively affect user experience.
  2. We could pick and choose certain things to bring over to the main page. Perhaps we could do this in a way that also leads users to click to the Advanced pages.
  3. We could move nothing, but focus on doing a better job of directing users to the player sub-pages (such as advanced batting and pitching, splits, and game logs).

We opted for the second option, but will also be looking to address the third option. The solution for the immediate job at hand is getting some advanced stats on the main player pages. But the fact that Mark (and other users) didn’t even know we had these advanced stats is a symptom of another issue—some users either are not noticing these sub-pages or they know about them but don’t think to use them (because they’re a click away).

This is a big deal because Baseball-Reference has a lot of users, but the super-users are the ones that have discovered the game logs, splits, and other advanced features. From there, they move on to Stathead to get even more powerful tools for their research. We want as many users discovering those features as possible so they can also turn into power users. So, in the future I’ll be looking to improve the player (and team and league) sub-navigation.

Now that we chose the path to explore, there were still different ways to proceed. One was to move the Player Value table (where we show WAR and its components) to the Advanced Batting page, but bring the most important columns (such as WAR, WAA, oWAR, dWAR, etc.) along with the most important columns from other Advanced Batting tables.

We began testing with that.

Francisco Lindor Advanced Stats Mockup

This early mockup tested well but some users showed a very strong preference for keeping the Player Value table where it was and adding a separate Advanced Stats table below it. Honestly, that was probably the right solution all along, but I wanted to see we could solve this without increasing the number of tables on player pages. We ended up adding one, but that’s fine.

There were several key things from this mockup that tested well, such as:

  1. The collection of stats we chose (which were the result of team discussions and also a survey we shared on Twitter).
  2. The addition of rOBA (our version of wOBA—Reference weighted OBA) and Rbat+ (our version of wRC+—based on the Rbat used in WAR). Despite the fact that these stats are brand new, I was impressed by how many guessed right away what they were.
  3. The links under the table to let users quickly jump to any table on the Advanced Batting page from the main player page. Not only does this help raise awareness of the Advanced Batting page, but also lets users know what tables are specifically on the page before they even go there.

The next version we tested kept all of these features, but put them in a separate Advanced Batting table. We also added base-running data, more batted ball data (such as the oft-requested Exit Velocity and Hard Hit %), and a row to display league averages for each stat (because users may not know what a good XBT% is).

That version of the mockup looked much like what you see today:

Francisco Lindor Advanced Stats

This version tested exceedingly well. Now it came down to building it. I asked Kenny Jackelen (@kennyjackelen on Twitter), Baseball-Reference’s developer, for a summary of the development process for a new feature like this. Kenny said he:

  1. Iterated multiple times with the team internally to get feedback on the table implementation (including how the table should render for players from different eras).
  2. Created new database tables for exit velocity data (which also powers the Hard Hit %)
  3. Added columns to existing tables to store rOBA and Rbat+ more permanently (previously these calculations were done as an intermediate step to get to WAR, so the database structure needed some updates to make it easier to pull them into the page-building process alongside other stats).
  4. Added logic to our play-by-play processing to assign batted balls a Pull/Center/Oppo location so that we can get a count of each type and compute the percentages for the Advanced Batting table
  5. Read a lot of slack messages in ALL CAPS from Adam D—like a marathon runner being handed a cup of water.

When it was ready, I got Mark back on Zoom to see his reaction. He said “it’s a 10.” He elaborated further, saying “It's not enough to be baseball’s best data aggregator. You have to present the information in a way that fans will be able to find it. I was honored that BRef and Adam took my suggestions to heart. The new player page designs put so many great pieces of data in easy to find places… near the top of the page.”

As a researcher, it was very fulfilling to come full circle with Mark. He went from tough love to delight.

As helpful as it was, not all user interviews revolve around tough love. Many users I have spoken with weren’t sure what to expect when they hopped on a call. Far more often than not, it’s just a casual conversation about baseball, the different ways people use the site, and what they’d like to be able to do.

I asked interview subject Jim Passon (@PassonJim on Twitter) if he had any thoughts on the interview process (so you don’t just have to take my word for it). He said “When Adam reached out to me to have a conversation about features that I’d like to see in the future, I couldn’t get the meeting set up quick enough. As expected, the meeting was awesome! I got to make a few suggestions, learn some new tricks, and catch a glimpse of the cool features that were already being developed for the site (which I absolutely loved). I now feel like I’m a part of my favorite site on the web... and that feels pretty good!”

Interviewee Jessica Brand (@JessicaDBrand on Twitter) echoed a similar sentiment, saying “I felt at ease, just discussing sports in depth in every which way with friends. It’s a great way to get those endorphins going to see and meet up with friends at your local stadium/arena/pitch you can’t necessarily see because of social distancing. Interviewing with Adam and Kenny provided the same warm and fuzzies.”

And honestly, in this time of social distancing and quarantine, hopping on the phone to talk about Baseball Reference with some of my favorite writers and analysts has been incredibly fulfilling. If you’d like to chat with me about how you’re using Baseball-Reference and Stathead, feel free to reach out at @baseballtwit on Twitter or go ahead and book a time on my calendar to chat.

Posted in Advanced Stats, Baseball-Reference.com, Data, Features, History, WAR | 2 Comments »

December 2020 WAR Update

14th December 2020

We recently fixed an issue where, because of the abbreviated 2020 season, we were not allocating enough wins to position players when calculating Wins Above Replacement. We have fixed this issue across Baseball-Reference. With this change, no position player gained more than 0.3 WAR, and no position player lost WAR. All pitcher WAR remained the same.

You can review the changes for each player here: https://docs.google.com/spreadsheets/d/18WY53wSt0GrBMMijLiIFMhVtvbmjuhbYNOaTvHfs-gE/edit?usp=sharing

If you have any questions or concerns, feel free to contact us through our feedback form.

Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Statgeekery, WAR | Comments Off on December 2020 WAR Update

2020 WAR Update

16th March 2020

As we approach the beginning of the 2020 season, we have made some updates to our Wins Above Replacement calculations.  You may notice some small changes to figures as you browse the site. As always, you can find full details on how we calculate WAR here.

Defensive Runs Saved Changes

Last week, we updated Defensive Runs Saved (DRS) totals across the site with new figures from Baseball Info Solutions.  The new methodology involves breaking down infielder defense using the PART system - assigning run values to Positioning, Air Balls, Range, and Throwing.  Under the new system, an infielder’s total DRS is the sum of his Air Balls, Range, and Throwing runs saved, while Positioning runs saved are credited to the team as a whole.  You can read more about the updates in the Sports Info Solutions blog.  The PART system applies to all infielders since 2013.

Folding these numbers into WAR, we see some significant changes for individual player seasons.  The 2019 Oakland A’s get even more recognition for defense on the left side of their infield, with shortstop Marcus Semien gaining 0.7 WAR and third baseman Matt Chapman gaining 1.6 WAR from the new DRS numbers, lifting both players above Mike Trout and into second and third place respectively on the 2019 AL WAR leaderboard.  Chapman’s 1.6 additional WAR represents the largest single-season change in this update.

On the other end of the spectrum, we see Adrian Beltre with the most significant drop in this update, losing 1.5 WAR in 2015.

Since we use DRS to measure the quality of a team’s defense, these new values also impact pitcher WAR values.  Team total DRS changed by as much as 46 runs for a given team and season - the 2019 Dodgers defense improved from 75 DRS to 121 DRS by non-pitchers under the new system.  Once applied to a specific pitcher, however, the changes to WAR are much smaller in magnitude than the changes to individual fielders. The most extreme example is Hyun-Jin Ryu, who pitched 182.2 innings in front of the 2019 Dodgers defense.  Considering the Dodgers defense to be 46 runs better across the entire season, and considering that Ryu was the pitcher for 13.52% of the Dodgers’ balls in play in 2019, we adjust our expected runs allowed for Ryu by 6.2 runs for the season. After following the rest of the steps in our pitching WAR calculation, the end result is a drop of 0.3 WAR for the season.  All other changes to pitching WAR from this change to team defense are smaller than Ryu’s 0.3 WAR drop in 2019.

Park Factors

Park factors for 2018 have been re-computed to include the 2019 season, since WAR uses a three-year average for park factors when computing pitching WAR.  The most significant change here is the Miami Marlins, whose pitching park factor rose from 90 to 95 (where <100 represents a pitcher’s park and >100 represents a hitter’s park).  José Ureña sees the biggest benefit from this, with his 2018 WAR rising by 0.7 wins. All other changes to pitching WAR from updated park factors are smaller than Ureña’s 0.7 WAR gain in 2018.

New Game Logs from Retrosheet (1904-1907)

Last month, we updated the site with new data from Retrosheet, including new game logs for players from 1904 to 1907.  Having game-level data allows us to be more precise in our WAR calculations, since we can consider the specific ballparks a pitcher played in and the opponents he faced.

Take Christy Mathewson in 1907 as an example.  Prior to this change, we used the league average (excluding his team) of 3.36 runs per nine innings as the expected quality of his opposition.  However, with game-level data, we can see that Mathewson’s actual opponents averaged 3.55 runs per nine innings, showing that Mathewson was probably used strategically and started more games against better opponents.  Indeed, Mathewson pitched in 10 of the Giants’ 22 games against the league’s best offense, the Pirates, as well as 7 of the Giants’ 22 games against the Cubs, the NL’s second-best offense. Against the Dodgers and Cardinals, who each struggled offensively and scored fewer than 3 runs per game, Mathewson pitched in just 8 games total.

Knowing this about his usage, we can set more accurate expectations for how many runs an average player would have allowed under Mathewson’s circumstances.  By adjusting the quality of his opposition, we expect an average pitcher to have allowed about 7 more runs over the course of the season, resulting in a bump of 0.9 WAR in 1907.  All other changes to pitching WAR from new game log data are smaller than Mathewson’s 0.9 WAR gain in 1907.

Baserunning and Double Plays from Play-by-Play Data (1931-1947)

When calculating runs from baserunning and double plays, we use play-by-play data from seasons where it is complete enough to credit players for things like scoring from first on a double, advancing from first to third on a single, and hitting into fewer double plays than expected.

In the past, we have taken play-by-play data into account back to 1948 for baserunning and double plays, because the data further back than that has been incomplete and could give players an advantage in their WAR simply by having more complete play-by-play records than their peers.  As this data has become more complete over time, we have moved this cutoff back to 1931. The data is still somewhat sparse for games that took place during World War II (1943-45), but we felt it was worth including those years as well.

Pete Reiser of the Brooklyn Dodgers was skilled at taking extra bases, and it showed in the play-by-play accounts.  In 1942, he took extra bases at a rate of 55%, compared to the league average of 45%. Additionally, the Dodgers were tied with the Cardinals as the league’s top scoring offense, so Reiser had many opportunities to put his speed to use.  He scored from first on doubles a league-leading ten times in just 15 opportunities, and also scored from second on a single 24 times, good for 5th in the NL that year, in just 29 opportunities. Using this play-by-play data while computing WAR gives Reiser an additional 1.2 WAR in 1942.  All other changes to batting WAR from this change are smaller than Reiser’s 1.2 WAR gain in 1942.

Caught Stealing Totals from Game Logs (1926-1940)

When crediting runners for how many runs they contributed with their baserunning, we take into account their stolen base and caught stealing totals.  Caught stealing totals are missing for many players between 1926 and 1940, but we have complete game logs for players in that span.

In the past, when we didn’t have a caught stealing total for a player, we would estimate how many times they were likely to have been caught stealing based on the league’s stolen base success rate and the ways the player reached base during the season.

We are now using actual caught stealing totals from the players’ game logs, so there are some changes for players who did considerably better or worse than we had been estimating.

Take, for example, Freddie Lindstrom.  In 1928, the Giants third baseman stole 15 bases, but his official season stat line does not have caught stealing available.  Previously, we had estimated that he was caught stealing 11.57 times, based on everything else we knew about his performance and the league he played in.  However, game logs indicate that Lindstrom was caught 21 times, nearly twice as often as we had estimated. This difference gets folded into our baserunning runs calculation and results in a drop of 0.4 WAR.  All other changes to batting WAR from this change are smaller than Lindstrom’s 0.4 WAR drop in 1928.

Biggest Career Movers

Hall of Famer Ernie Lombardi sees the biggest change to his career WAR with this update, sinking from 46.8 WAR to 39.5 WAR, a drop of 7.3 wins.  The largest gain goes to infielder Lonny Frey, who picks up 5.2 wins. Both these players played in the 1930s and 1940s and saw big changes because of their baserunning.  Lombardi is known for being one of the slowest runners in baseball history, and this update shows that the numbers back that reputation. Frey was a fast runner in an era where stolen bases were rare, so he has been underrated to this point when it comes to his baserunning contributions.

On the mound, previously cited Hall of Famer Christy Mathewson is the big winner.  As discussed above, his WAR now recognizes how his manager would use him against tougher opponents, and he sees his career WAR jump by 2.2 wins.  Barney Pelty experiences the biggest drop of 1.9 wins.

We’ve highlighted some of the more extreme changes here, but to see full lists of the largest changes to season and career WAR totals, please see the spreadsheet here.

We're very excited about these new additions and hope you enjoy them as well. Thanks to Baseball Info Solutions for their contributions. Please let us know if you have any comments, questions or concerns.

Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Features, History, Leaders, Play Index, Statgeekery, WAR | 5 Comments »

2019 WAR Update

21st March 2019

As we approach the beginning of the 2019 season, we have made some updates to our Wins Above Replacement calculations.  You may notice some small changes to figures as you browse the site. As always, you can find full details on how we calculate WAR here.

Openers

Last season, the Tampa Bay Rays popularized the concept of the opener, where the first pitcher of the game is expected to pitch considerably less than a typical starting pitcher.  The opener is followed by a “headliner” or “bulk guy,” who enters the game after the opener but takes on responsibilities similar to a traditional starting pitcher. The Rays found success with this approach, and several other teams followed suit.

Our Wins Above Replacement calculation treats starting pitchers and relief pitchers differently, since relief pitchers have much lower ERAs than starters.  The opener strategy throws a wrinkle into this, since the opener is not expected to go deep into the game and the headliner is, so we have a starting pitcher who is behaving more like a relief pitcher and vice versa.

Tom Tango posted some thoughts on this last year, and the discussion in the comments of that post produced a working definition for the opener:

  1. Determine if we have an opener.  This pitcher must start the game and have either at most 2 innings pitched (6 outs), or at most 9 batters faced.
  2. Determine if we have a headliner. This pitcher must meet two criteria:
  3. Length of appearanceAt least 4 innings pitched (12 outs), or at least 18 batters faced
  4. Order of appearanceThey are the first reliever, OR they are the second reliever, but the first reliever entered mid-inning, and the second reliever started the following inning

 

If both these pitchers exist, then we have a game with an opener and a headliner.  Both pitchers must exist; you cannot have an opener without a headliner, and vice versa.

Using this definition, we have updated our WAR calculation to treat openers like relievers and headliners like starters.  This change has been applied to all seasons since 1960, the first year we apply a starter/reliever adjustment.

Ryan Yarbrough, the Rays’ most frequent headliner, is an instructive case.  He pitched 38 games and 147.1 innings, but started just 6 times.  By the above definition, 16 of his relief appearances were as a headliner.  Prior to this adjustment, the Rays’ rookie had 0.9 WAR for 2018. After the adjustment, Yarbrough has 1.5 WAR.  The new calculation recognizes that Yarbrough is behaving more like a traditional starting pitcher, and holds his performance to the same standard it would if Yarbrough had started those games.

Park Factors

Park factors for recent seasons have been re-computed to be three-year rolling averages. For instance, 2017 Park Factors now encompass 2016-2018. This is something that needs to be done each year when the season ends.

Catcher Defense Prior to 1953

With help from Sean Smith of baseballprojection.com (and of an unnamed team front office) and baserunning statistics from Pete Palmer, we now have incorporated catcher defense for the years 1890 through 1952 based on stolen bases, caught stealing, errors, passed balls, and, from 1925 on, wild pitches.  Prior to this update, these players’ defensive abilities were judged only based on errors and passed balls.

Duke Farrell is a particularly noteworthy beneficiary of this change.  His career WAR rises by nearly 8 wins, because he played in an era (1888-1905) with a lot of stolen base attempts and did a better job of throwing out runners than his contemporaries.

This change also impacts pitchers’ WAR figures, since we have more information about the quality of defenses to take into account.  For instance, Jack Taylor and Kid Nichols of the 1904 Cardinals see their WAR numbers rise by more than a win each after accounting for the fact that their catchers threw out fewer runners than the rest of the league.  Indeed, the Cardinals’ primary backstop Mike Grady saw his WAR drop by two wins with this update.

On the flipside, legendary pitcher Cy Young loses more than 4 wins over his career after accounting for the above-average work his teammates did behind the plate throughout his career.

We’ve highlighted some of the more extreme changes here, but to see full lists of the largest changes to season and career WAR totals, please see the spreadsheet here.

 

Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Statgeekery, WAR | 12 Comments »

Adding WAR to Baseball-Reference’s Birthdays Page

24th August 2018

Baseball-Reference keeps track of the birthdays of every active MLB player as well as the majority of historical ballplayers on our site. You can access today's birthdays by clicking on the Happy Birthday/Born On This Day link on Baseball-Reference's front page. You could already sort birthdays by birth year, years played in MLB, All-Star Game selections and other basic statistics. But now, we've added our Wins Above Replacement metric as an additional stat to sort by.

Since today's August 24th, let's give a spotlight to the top 10 players born on this day, ranked by WAR:

Players Born On August 24 Table
Rk Name Born Yrs From To WAR
Franchises
1 Cal Ripken Jr. HOF 1960 21 1981 2001 95.9 BAL
2 Harry Hooper HOF 1887 17 1909 1925 53.5 BOS,CHW
3 Tim Salmon 1968 14 1992 2006 40.6 ANA
4 Brett Gardner 1983 11 2008 2018 37.6 NYY
5 Hank Gowdy 1889 17 1910 1930 18.2 SFG,ATL
6 Tony Bernazard 1956 10 1979 1991 13.1 WSN,CHW,SEA,CLE,OAK,DET
7 Hal Woodeshick 1932 11 1956 1967 9.6 DET,CLE,MIN,TEX,HOU,STL
8 Jimmy Walsh 1887 6 1912 1917 5.8 OAK,NYY,BOS
9 Enrique Hernandez 1991 5 2014 2018 5.5 HOU,FLA,LAD
10 Luis Sanchez 1953 5 1981 1985 3.9 ANA
Provided by Baseball-Reference.com: View Original Table
Generated 8/24/2018.

If you have any questions or suggestions, feel free to contact us through our feedback form.

Posted in Announcement, Baseball-Reference.com, WAR | Comments Off on Adding WAR to Baseball-Reference’s Birthdays Page

2018 WAR Update

15th March 2018

As you browse Baseball Reference, you might notice some subtle changes to WAR figures on the site. There are four main reasons for this:

  1. Park factors for recent seasons have been re-computed to be three-year rolling averages. For instance, 2016 Park Factors now encompass 2015-2017. This is something that needs to be done when seasons end.
  2. We've incorporated restated and expanded fielding statistics from Sports Info Solutions. SIS's Defensive Runs Saved forms the basis for our Defensive WAR calculations since 2003. From 2011 on they recalibrated data using their timer measures to measure ball hang time. There was also some recalculation based on changing shift methodology. Though we're now publishing their catcher framing stat (Strike Zone Runs Saved), we have not incorporated it into WAR at this time.
  3. Pitchers who received time as position players (whether PH or in the field) are now being treated as part-time pitchers and part-time position players. Previously we treated them as full-time pitchers. Some pitchers like Red Ruffing, Bob Lemon and Jim Kaat appeared in many games as a position player, pinch hitter or pinch runner. We used to credit these PAs as pitchers, which overvalued their offensive contributions. To handle this, we compute a percentage of time as a non-pitcher and make an adjustment.
  4. Also, we have incorporated a good deal more Retrosheet data which has affected the years we can compute more advanced fielding and baserunning measures. We're now able to roll these measures back to 1953. Another important change is that with Retrosheet gamelogs back to 1908, we can now use their IP data back to that year to get starter/relief IP splits. Some pitcher WAR changes for 1908-12 are due to WAR now being calculated using gamelog IP rather than the "official" total listed on the player's stat line. The biggest difference here was the appropriately named Bugs Raymond. The "official" record credits him with 324.1 IP that season, but the gamelogs come out to 304.1, which significantly impacted his WAR calculation (see below). For further reading on discrepancies between "official" records and more recently produced gamelogs, please read this excellent explainer by Retrosheet's Dave Smith.

For further details on WAR and its calculation, please see this WAR explainer.

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Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Statgeekery, Uncategorized, WAR | 8 Comments »

Pitch Framing Measures Added to Baseball Reference

8th March 2018

Our friends at Sports Info Solutions (formerly known as Baseball Info Solutions) have provided us with a pitch framing measure back to 2011, which we have added to Baseball Reference. Before I explain any further, if you're unfamiliar with the concept of pitch framing please read Mike Fast's 2011 article on the topic and Ben Lindbergh's 2013 follow up.

The stat that we have added is called Strike Zone Runs Saved. It represents the runs saved by catcher framing. In our tables, it's labeled RszC and it's available from 2011 to the present. While this statistic is a potential component of Defensive Runs Saved (and therefore WAR), please note that we have elected to not integrate this number into DRS (or WAR) at this time. We may elect to do so in the future, but for now we agree with Bill James's stance that waiting for further research is a good idea.

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Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, Features, Statgeekery, WAR | 3 Comments »

A Discussion of WAR Wherein I Ardently Attempt to Avoid any WAR-Related Puns

21st November 2017

This article assumes a lot of prior knowledge about the discussion of Wins Above Replacement, you can catch up here

First off, none of us are here without Bill James. We are all at our very best merely Chaucer or Joyce to his Shakespeare. All sabermetrics predating him flowed into his work and all sabermetrics after him carries echoes of his work.

To the discussion at hand.
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Posted in Academics, Advanced Stats, Baseball-Reference.com, Stat Questions, Statgeekery, Trivia, WAR | 9 Comments »