![\"\"](\"https:\/\/www.rawchili.com\/mlb\/wp-content\/uploads\/2026\/04\/Zac-Gallen-2026.jpg\")
Mark J. Rebilas-Imagn Images<\/p>\n\t\t\t\tMark J. Rebilas-Imagn Images<\/p>\n
Last week, Dan Szymborski looked at<\/a> how much a team\u2019s fortunes can change in the first month of the season. That old truism \u2013 you can\u2019t win the World Series in April, but you can lose it \u2013 turns out to, in fact, be true. Dan\u2019s research found that even teams we think are good \u2013 those projected to win 90 or more games \u2013 had meaningfully worse results after a bad April, even if their actual talent remained the same. <\/p>\n In other words, those early losses really do count. But I like to look at things from a glass-half-full perspective, so my takeaway was that there\u2019s still plenty of time to fix a bad start, because it\u2019s still early in the season. But how to fix it? That\u2019s a trickier question. Luckily, \u201cthat\u2019s a tricky question\u201d is just FanGraphs for \u201cthat\u2019s a fun thing to write an article about,\u201d so I\u2019ve got answers for you.<\/p>\n I came up with a method that let me look at all 30 teams at once. First, I took our playoff odds, with current standings, for every team in baseball. Then, I went through and found each team\u2019s weakest position. I did this separately for pitchers and hitters. How\u2019d I do it? I sure hope you\u2019re in the mood for some methodological exposition, but if not, you can probably skip the next three paragraphs and get right to the good stuff.<\/p>\n For each batting position on each team, I summed the projected WAR from our Depth Charts. I used these to create league-wide averages for each position, important considering the average shortstop projection, for example, is much better than the average first base projection. Then I compared each position for each team to the league average and noted the one that fell furthest short. To improve that position, I created a synthetic league-average player and assigned that player either the most plate appearances that any player on that team was projected for at that position, or 600 plate appearances, whichever was higher. I then rebuilt the depth chart behind our synthetic player by sliding down whoever was left and noted the difference in WAR between the new depth chart and the old one. I divided that change in WAR by the number of games remaining in the season to turn it into a change in winning percentage, bumped that team\u2019s true talent winning percentage by that amount, and then re-ran our playoff odds 20,000 times with its new, improved squad, keeping the talent level of all other teams constant, to calculate what its odds would look like post-trade.<\/p>\n For starting pitchers, I constructed a league-average pitcher by averaging the top seven options on each team\u2019s depth chart to create one global league-average WAR per inning pitched. For upgrades, I went down each rotation in order until I found a player with a lower projected WAR per inning pitched than that league average. I replaced that pitcher with the league-average option, then used a waterfall method. The displaced pitcher took the spot of the first pitcher below him on the depth chart who projected to be worse than him on a per-inning basis. Then that displaced pitcher followed the same procedure, over and over until I rebuilt the entire depth chart. In this way, injured but talented players didn\u2019t get bumped down the playing time ladder undeservedly. I then took the difference between how much WAR the pre- and post-upgrade rotations projected for, turned that into increased winning percentage, and re-ran the playoff odds.<\/p>\n I didn\u2019t subtract any players from any teams when I re-ran the odds. I also didn\u2019t re-run platoons or account for risk of injury. These odds are intended as a quick check of how much teams might improve by patching their weakest holes, not as a complete functioning model of the major leagues. I think their estimates are reasonable. I wouldn\u2019t defend any of them as being perfect, but I do think that this method does an excellent job of identifying low-hanging fruit.<\/p>\n
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Sorry, did your eyes glaze over there? That was a lot of methodology in a few dense paragraphs. But whether you felt like following along or not, the upshot is that I came up with a list of how much each team in baseball could improve by replacing its weakest position with a league-average player, and how much that would change its playoff odds. For example, the AL East could use some shortstops, but not every team could use them equally:<\/p>\n Team Upgrade From One League-Average Regular, AL East<\/p>\n Team Yankees Blue Jays Red Sox Orioles Rays The Blue Jays and Orioles have well-rounded squads, which explains why the WAR upgrade from shoring up their weakest spot is minimal. In fact, all of Baltimore\u2019s non-pitcher positions are projected to be above average, so we can\u2019t upgrade its roster with a league-average player, even in center field, the team\u2019s weakest spot. That\u2019s why you see \u201cn\/a\u201d in the table above. The Yankees project to receive nearly as large of a WAR upgrade as the Red Sox from this exercise, but since they\u2019re already quite likely to make the playoffs, their playoff odds change by less. And the Rays have the largest potential gain in the majors. They\u2019re right on the fringe of the playoff race, and yet they have the worst projected shortstop depth chart in the majors.<\/p>\n Six teams could boost their playoff odds by more than five percentage points by upgrading their weakest position to league average:<\/p>\n Team Upgrade From One League-Average Regular<\/p>\n Team Rays Diamondbacks Braves Guardians Red Sox Padres I did the same thing for starting pitchers, and this time we\u2019ll flip leagues and coasts and use the NL West as an example:<\/p>\n Team Upgrade From One League-Average SP, NL West<\/p>\n Team Dodgers Diamondbacks Padres Giants Rockies
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\n <\/p>\n
\nUpgrade Position
\nWAR From Upgrade
\nPlayoff% Increase From Upgrade<\/p>\n
\nSS
\n0.8
\n3.1%<\/p>\n
\nSS
\n0.3
\n1.8%<\/p>\n
\nSS
\n1.0
\n5.7%<\/p>\n
\nCF
\nn\/a
\nn\/a<\/p>\n
\nSS
\n1.7
\n10.0%<\/p>\n
\nUpgrade Position
\nWAR From Upgrade
\nPlayoff% Increase From Upgrade<\/p>\n
\nSS
\n1.7
\n10.0%<\/p>\n
\n1B
\n1.4
\n7.6%<\/p>\n
\nSS
\n1.3
\n7.1%<\/p>\n
\nSS
\n1.5
\n6.9%<\/p>\n
\nSS
\n1.0
\n5.7%<\/p>\n
\n1B
\n1.4
\n5.5%<\/p>\n
\nRotation Spot Upgraded
\nWAR From Upgrade
\nPlayoff% Increase From Upgrade<\/p>\n
\nSP6
\n0.3
\n0.2%<\/p>\n
\nSP1
\n0.7
\n4.0%<\/p>\n
\nSP4
\n1.1
\n4.4%<\/p>\n
\nSP2
\n1.0
\n4.0%<\/p>\n
\nSP1
\n1.2
\n0.1%<\/p>\n