Today is opening day, the start of the new season. How will it end up? Using RotoValue player projections for each team, I’ve estimated win totals based on projected runs scored and runs allowed. Here are the standings: AL East Won Lost Pct RS RA Yankees 87.1 74.9 0.538 665 617 Blue Jays 82.1 79.9… Continue reading MLB Crystal Ball 2021
Reverse Platoon Splits – Before and After
Tom Tango asked someone to see what players with at least a 30-point wOBA reverse platoon split in their first 2500 plate appearances did for the rest of their careers, and Sean Foreman kindly provided split data from his database. So I hacked together a Perl script to read his data and analyze it. The script lets… Continue reading Reverse Platoon Splits – Before and After
A few years back Brian Kenney introduced the hashtag #KillTheWin, which still lives on. Baseball fans point out egregious cases of a pitcher getting a win in a game despite pitching poorly. While the win might have been a useful metric for pitchers in the dead-ball era, when starters completed more than half their starts… Continue reading #FixTheWin
Comparing Projected HR leaders to actual
Tom Tango asked an interesting question on Twitter yesterday: The odds of the projected HR leader actually leading the league is an interesting question. I’ve been doing projections since 2011, so I thought I’d sweep my database for the RotoValue projections and see what that history was. That gives me just five years, but it… Continue reading Comparing Projected HR leaders to actual
FiveThirtyEight Baseball Division Champs Puzzle
Update: I’ve added a link to the Perl progam I used to do these simulations. Oliver Roeder presents a weekly puzzler on FiveThirtyEight, and this week it was a baseball-themed puzzle. Assume a sport (say, “baseball”) in which each team plays 162 games in a season. Also assume a “division” (e.g. the “AL East”) containing 5 teams, each… Continue reading FiveThirtyEight Baseball Division Champs Puzzle
Comparing 2014 Projections – ERA and WHIP
Yesterday I ran comparisons of several projections systems for an all-inclusive batting statistic, wOBA. Today I’m running the same tests, computing root mean square error (RMSE) and mean absolute error (MAE), for two commonly used fantasy statistics, ERA and WHIP. These tests are bias-adjusted, so what matters is a player’s ERA or WHIP relative to the overall average of that… Continue reading Comparing 2014 Projections – ERA and WHIP
Comparing 2014 Projections – wOBA
In the past three years I’ve done reviews of baseball projections systems with actual data for those systems for which I could get data. Will Larson maintains a valuable site of projections from many different sources, and most of the sources I’m comparing are from that. As in the past, I’m computing root mean square error (RMSE) and mean absolute error (MAE) for… Continue reading Comparing 2014 Projections – wOBA
Points-based scoring in MLB?
Tom Tango proposed a points system for MLB, allowing for ties and not using extra innings. He proposed giving each team 5 points to start. Extra inning games stayed at 5 points, treating them as a tie. If you win by 1, you get 1 more point; winning by 2 gets 3 more points, and winning by 3… Continue reading Points-based scoring in MLB?
Who gains and loses if we #RedefineTheWin?
Wednesday I introduced a page comparing traditional wins and losses with those assigned by a points system proposed by Tom Tango. Tom agreed with my modification of run values, using -5 in win points and 10 in loss points. Today I’d like to highlight some of the biggest changes in record using this proposal would… Continue reading Who gains and loses if we #RedefineTheWin?
While Brian Kenny turned #KillTheWin into a Twitter meme, Tom Tango has proposed redefining it. Rather than using the old definition, he suggests computing “win points” and “loss points” for each game, and then giving the player with the most win points on the winning team a win, while the player with the most loss points on the losing… Continue reading #RedefineTheWin