A Further Discussion on the Memorial Day Checkpoint
Yesterday, I published an article about Memorial Day as it relates to the baseball standings. In sum, I wrote about the baseball adage that one should not check the standings until Memorial Day. Using data from 2010 to 2018, I looked at the correlation between Memorial Day winning percentage and end-of-season winning percentage and constructed a linear regression line to fit the data.
Within the piece, I used the regression equation to discuss full-season scenarios for the Twins and Nationals, two teams that have surprised — albeit for different reasons — this season. The response to the article was interesting, and some asked for me to take a look at full-season projections for all 30 teams based on the regression.
This sortable chart does exactly that:
| Team | Win Percentage | Projected Final | Projected Wins | Projected Losses |
|---|---|---|---|---|
| Twins | 0.667 | 0.605 | 97.9 | 64.1 |
| Astros | 0.660 | 0.600 | 97.2 | 64.8 |
| Yankees | 0.646 | 0.591 | 95.8 | 66.2 |
| Dodgers | 0.640 | 0.588 | 95.2 | 66.8 |
| Cubs | 0.617 | 0.573 | 92.9 | 69.1 |
| Rays | 0.609 | 0.568 | 92.1 | 69.9 |
| Phillies | 0.571 | 0.545 | 88.2 | 73.8 |
| Brewers | 0.569 | 0.543 | 88.0 | 74.0 |
| Braves | 0.540 | 0.525 | 85.1 | 76.9 |
| Red Sox | 0.531 | 0.520 | 84.2 | 77.8 |
| Pirates | 0.522 | 0.514 | 83.3 | 78.7 |
| Indians | 0.521 | 0.514 | 83.2 | 78.8 |
| Padres | 0.520 | 0.513 | 83.1 | 78.9 |
| Rangers | 0.511 | 0.507 | 82.2 | 79.8 |
| Cardinals | 0.510 | 0.507 | 82.1 | 79.9 |
| Athletics | 0.500 | 0.501 | 81.1 | 80.9 |
| Diamondbacks | 0.500 | 0.501 | 81.1 | 80.9 |
| Mets | 0.479 | 0.487 | 79.0 | 83.0 |
| Rockies | 0.468 | 0.481 | 77.9 | 84.1 |
| Angels | 0.458 | 0.474 | 76.8 | 85.2 |
| White Sox | 0.458 | 0.474 | 76.8 | 85.2 |
| Reds | 0.449 | 0.469 | 75.9 | 86.1 |
| Mariners | 0.442 | 0.464 | 75.2 | 86.8 |
| Giants | 0.438 | 0.462 | 74.8 | 87.2 |
| Blue Jays | 0.408 | 0.443 | 71.8 | 90.2 |
| Tigers | 0.391 | 0.433 | 70.1 | 91.9 |
| Nationals | 0.388 | 0.431 | 69.8 | 92.2 |
| Royals | 0.347 | 0.405 | 65.6 | 96.4 |
| Marlins | 0.326 | 0.392 | 63.5 | 98.5 |
| Orioles | 0.306 | 0.380 | 61.5 | 100.5 |
I will say that you should take these projections with a grain of salt, and I’d recommend for you to look at our actual projected standings for a better estimate of the full-season results. These projections are based on a regression line that only could account for 57% of the variability in full-season results.
This means that these expected win totals could be off, and they could be off pretty significantly. As I wrote in my article on Wednesday, we’ve seen teams outperform their expectation by as many as 112 points (2012 Dodgers) or underperform their expectation by as many as 129 points (2013 Astros). With this in mind, let’s call these two extremes our best- and worst-case scenarios, respectively. Now let’s put those best- and worst-case scenarios into a chart for all 30 teams:
| Team | Win Percentage | Projected Final | Best Case | Best Case Wins | Worst Case | Worst Case Wins |
|---|---|---|---|---|---|---|
| Twins | 0.667 | 0.605 | 0.717 | 116.1 | 0.476 | 77.0 |
| Astros | 0.660 | 0.600 | 0.712 | 115.4 | 0.471 | 76.3 |
| Yankees | 0.646 | 0.591 | 0.703 | 114.0 | 0.462 | 74.9 |
| Dodgers | 0.640 | 0.588 | 0.700 | 113.4 | 0.459 | 74.3 |
| Cubs | 0.617 | 0.573 | 0.685 | 111.0 | 0.444 | 72.0 |
| Rays | 0.609 | 0.568 | 0.680 | 110.2 | 0.439 | 71.2 |
| Phillies | 0.571 | 0.545 | 0.657 | 106.4 | 0.416 | 67.3 |
| Brewers | 0.569 | 0.543 | 0.655 | 106.2 | 0.414 | 67.1 |
| Braves | 0.540 | 0.525 | 0.637 | 103.3 | 0.396 | 64.2 |
| Red Sox | 0.531 | 0.520 | 0.632 | 102.4 | 0.391 | 63.3 |
| Pirates | 0.522 | 0.514 | 0.626 | 101.4 | 0.385 | 62.4 |
| Indians | 0.521 | 0.514 | 0.626 | 101.3 | 0.385 | 62.3 |
| Padres | 0.520 | 0.513 | 0.625 | 101.2 | 0.384 | 62.2 |
| Rangers | 0.511 | 0.507 | 0.619 | 100.3 | 0.378 | 61.3 |
| Cardinals | 0.510 | 0.507 | 0.619 | 100.2 | 0.378 | 61.2 |
| Athletics | 0.500 | 0.501 | 0.613 | 99.2 | 0.372 | 60.2 |
| D-backs | 0.500 | 0.501 | 0.613 | 99.2 | 0.372 | 60.2 |
| Mets | 0.479 | 0.487 | 0.599 | 97.1 | 0.358 | 58.1 |
| Rockies | 0.468 | 0.481 | 0.593 | 96.0 | 0.352 | 57.0 |
| Angels | 0.458 | 0.474 | 0.586 | 95.0 | 0.345 | 55.9 |
| White Sox | 0.458 | 0.474 | 0.586 | 95.0 | 0.345 | 55.9 |
| Reds | 0.449 | 0.469 | 0.581 | 94.1 | 0.340 | 55.0 |
| Mariners | 0.442 | 0.464 | 0.576 | 93.4 | 0.335 | 54.3 |
| Giants | 0.438 | 0.462 | 0.574 | 93.0 | 0.333 | 53.9 |
| Blue Jays | 0.408 | 0.443 | 0.555 | 89.9 | 0.314 | 50.9 |
| Tigers | 0.391 | 0.433 | 0.545 | 88.2 | 0.304 | 49.2 |
| Nationals | 0.388 | 0.431 | 0.543 | 87.9 | 0.302 | 48.9 |
| Royals | 0.347 | 0.405 | 0.517 | 83.8 | 0.276 | 44.7 |
| Marlins | 0.326 | 0.392 | 0.504 | 81.7 | 0.263 | 42.6 |
| Orioles | 0.306 | 0.380 | 0.492 | 79.6 | 0.251 | 40.6 |
As you can probably see, this doesn’t tell us much. The Twins aren’t going to win 116, the Rockies aren’t going to win 96, and the Orioles won’t win 80. But if you consider these to be the absolute high-bound win totals for most teams — something like the 99.6th percentile, considering only one team out of the 270-team sample (0.4%) we have from our initial dataset was able to achieve these levels of outperforming the expectation — things begin to make more sense.
A troubling figure is the one for Nationals fans with hope; if their 99.6th percentile projection is only 88 wins, I think we can begin to safely assume that 2019 is going to be a lost season in D.C. On the flip side, if you’re a Twins fan and you see that their 0.4th percentile projection is 77 wins, you’d have to be feeling pretty good.
Realistically, no team is going to play to these projections. Let’s use the 25th and 75th percentile instead, as those would still be within a truly possible range of outcomes. Based on the 270-team sample again, we would find the 75th percentile residual to be +29 points of win percentage and the 25th percentile residual to be -31 points of win percentage. Let’s construct a third chart with these scenarios:
| Team | Win Percentage | Projected Final | 75th Percentile | 75th Wins | 25th Percentile | 25th Wins |
|---|---|---|---|---|---|---|
| Twins | 0.667 | 0.605 | 0.634 | 102.6 | 0.574 | 92.9 |
| Astros | 0.660 | 0.600 | 0.629 | 101.9 | 0.569 | 92.2 |
| Yankees | 0.646 | 0.591 | 0.620 | 100.5 | 0.560 | 90.8 |
| Dodgers | 0.640 | 0.588 | 0.617 | 99.9 | 0.557 | 90.2 |
| Cubs | 0.617 | 0.573 | 0.602 | 97.6 | 0.542 | 87.9 |
| Rays | 0.609 | 0.568 | 0.597 | 96.8 | 0.537 | 87.1 |
| Phillies | 0.571 | 0.545 | 0.574 | 92.9 | 0.514 | 83.2 |
| Brewers | 0.569 | 0.543 | 0.572 | 92.7 | 0.512 | 83.0 |
| Braves | 0.540 | 0.525 | 0.554 | 89.8 | 0.494 | 80.1 |
| Red Sox | 0.531 | 0.520 | 0.549 | 88.9 | 0.489 | 79.2 |
| Pirates | 0.522 | 0.514 | 0.543 | 88.0 | 0.483 | 78.3 |
| Indians | 0.521 | 0.514 | 0.543 | 87.9 | 0.483 | 78.2 |
| Padres | 0.520 | 0.513 | 0.542 | 87.8 | 0.482 | 78.1 |
| Rangers | 0.511 | 0.507 | 0.536 | 86.9 | 0.476 | 77.2 |
| Cardinals | 0.510 | 0.507 | 0.536 | 86.8 | 0.476 | 77.1 |
| Athletics | 0.500 | 0.501 | 0.530 | 85.8 | 0.470 | 76.1 |
| D-backs | 0.500 | 0.501 | 0.530 | 85.8 | 0.470 | 76.1 |
| Mets | 0.479 | 0.487 | 0.516 | 83.7 | 0.456 | 73.9 |
| Rockies | 0.468 | 0.481 | 0.510 | 82.5 | 0.450 | 72.8 |
| Angels | 0.458 | 0.474 | 0.503 | 81.5 | 0.443 | 71.8 |
| White Sox | 0.458 | 0.474 | 0.503 | 81.5 | 0.443 | 71.8 |
| Reds | 0.449 | 0.469 | 0.498 | 80.6 | 0.438 | 70.9 |
| Mariners | 0.442 | 0.464 | 0.493 | 79.9 | 0.433 | 70.2 |
| Giants | 0.438 | 0.462 | 0.491 | 79.5 | 0.431 | 69.8 |
| Blue Jays | 0.408 | 0.443 | 0.472 | 76.5 | 0.412 | 66.8 |
| Tigers | 0.391 | 0.433 | 0.462 | 74.8 | 0.402 | 65.1 |
| Nationals | 0.388 | 0.431 | 0.460 | 74.5 | 0.400 | 64.8 |
| Royals | 0.347 | 0.405 | 0.434 | 70.3 | 0.374 | 60.6 |
| Marlins | 0.326 | 0.392 | 0.421 | 68.2 | 0.361 | 58.5 |
| Orioles | 0.306 | 0.380 | 0.409 | 66.2 | 0.349 | 56.5 |
I still caution you when looking at these charts; they are not adjusted for team strength (or even run differential), as they just use previous team data to estimate the full scenarios. But these results paint what appears to be a pretty decent picture of where the league stands today. The Twins continue to look like the favorites to win AL Central, don’t they?
With that said, I am brought to a second question: if a team is in a playoff spot on Memorial Day, do they tend to hang on to the spot by the end of the season?
I went back to my sample of Memorial Day and Final Standings, and I looked at the results from 2012 through 2018. This represents every team who has played in the era of two Wild Cards. I found that of the 70 teams that have made the playoffs in those seven years, 46 of them held a playoff spot on Memorial Day. That’s 66%.
That would mean between six and seven of the teams who are already in a playoff spot as of Memorial Day will still be in a playoff spot by the end of the season. We can probably begin to guess those teams. The Dodgers have a six-game lead in the NL West, the Twins have a seven-game lead in the AL Central, and the Astros have a seven-and-a-half-game lead in the AL West. Those three teams are more or less locks to make the playoffs, and our odds reflect that; those three teams all have greater than a 90% chance to continue into October. The Yankees do too, as they currently have a two-game lead in the AL East.
With those, we’re already at four of our six or seven teams, so while 66% might sound like a lot, all those teams who had commanding divisional leads on Memorial Day tended not to fall out of the playoffs altogether. Bubble teams stay as bubble teams, and those teams will continue to shuffle in the standings as the season goes on.
The last topic I want to discuss is the predictiveness of Memorial Day records. As one reader of the original piece kindly pointed out, a comparison of the Memorial Day winning percentage and a team’s final winning percentage doesn’t have much predictive power. This is because a team’s final win percentage includes the games they played before Memorial Day, so there is double-counting involved. For my initial question, “Is Memorial Day the time to check the standings?” the double-counting works fine. I wasn’t looking to determine how predictive a team’s Memorial Day record actually is, I just wanted to figure how well teams finished out after their early-season performance.
This distinction is important, and our Nationals example can prove exactly why. The Nationals are currently 19-30 and in a nine-game hole in the NL East. Even if the Nationals finish their season by going 62-51, which represents a pretty solid .549 win percentage (89-win pace), they’d only finish the year 81-81. The Memorial Day record didn’t do a great job of predicting the Nationals’ rest-of-season record, but it did do a better job of predicting the Nationals’ full-season record.
So, let’s take a look at the predictive power of a team’s Memorial Day record:

To be blunt, it’s not great. There’s a moderate correlation here, evidenced by our r-value. But our regression line can only explain about 25% of the variability in a team’s rest-of-season win percentage, so there’s still a lot of change that can happen over the remainder of the baseball season, as expected.
What does this tell us, in combination with yesterday’s scatterplot which showed a much stronger correlation between Memorial Day winning percentage and full season winning percentage? Well, it tells us that teams can build themselves a large cushion (a la the Twins) by Memorial Day and ride that to full-season success. Conversely, it tells us that teams can be buried (a la the Nationals) by Memorial Day, and even with a rest-of-season turnaround, they probably still won’t be successful overall. But a team’s record on Memorial Day alone doesn’t necessarily tell us how they will play over the remaining games. That small distinction is extremely important when trying to answer my initial question. Yes, Memorial Day standings are meaningful, but no, they don’t do a great job of telling us how the teams will play over the remaining 110 or so games.
Devan Fink is a Contributor at FanGraphs. You can follow him on Twitter @DevanFink.
Probabilities really shouldn’t be added to other probabilities in this way. It might be better to work on the log-odds scale and do some transformations. This is one of the reasons why you end up with crazy best-case win totals like 116 for the Twins.
I don’t think 116 wins is all that crazy as a best-case scenario – the Twins got off to a really hot start, and if they have a hot (as in best-in-a-decade hot) rest of the season, you arrive at a record win total. Now, obviously (and as the author stated), they are almost guaranteed to finish with fewer than 116 wins, and I agree that it maybe isn’t the best math being done, but as absolute best- and worst-case scenarios I think the numbers given are fine and actually kind of illuminating regarding what it takes to end up with truly historic win totals at either end of the scale.
Additionally, he was clear, multiple times, about the ways in which the results might be skewed
“maybe isn’t the best math being done,”
Understatement of the year.
The Twins are 33-16, so getting to 116 wins would require them to go 83-30 for the rest of the season. Nothing is impossible, but I don’t see how that could happen. Adding 112 points to your winning percentage is much easier when your winning percentage starts of low. This analysis doesn’t account for this fact. Devan does do a good job of hedging his bets and I don’t think he is too misleading here, but why not do better analysis if the opportunity presents itself?
In 2017, the Dodgers had a 50 game stretch when they went 43-7 (and that beat a 42-8 run they had in 2013) – so if the Twins match that, they would then need to only go 36-23 the rest of the way, which… yeah 83-30 is difficult.
To me, saying the absolute best case scenario for the Twins, who have the best record, is to tie the all time wins record seems quite nice.
I dont know. After today, I’d place a pretty large wager on the under against the Tigers worst case 49.2 win total.
The definition of idiocy is doing the same thing over and over and expecting different results. As long as the Nationals continue to think that they can go to the bullpen at any stage of the game and expect to win is, by definition, the very epitome of that type of thinking. The modern theory of pitcher usage is haunting the Nationals like no team ever before. Changing the approach might work, or it might not, but there was a time when good pitchers were not automatically replaced by stiffs at a predetermined point of the game. What are they trying to do? If the Nats think that this might save Strasburg’s arm I have news for them, the facts are that he has pitched more than 147 innings exactly once since 2014. Let his arm fall off if that is all the production you are going to get anyway. It might be better to actually win a game once in a while.
It’s almost like the author doesn’t know this exists…
https://www.fangraphs.com/depthcharts.aspx?position=Standings
Two thoughts on the bounding analysis. 25%-75% is too restrictive, particularly to those teams near 500. For instance I find it hard to believe that the best case scenario for the Mets, currently a game below 500, is not even two games over 500. However for the extremes it does make some sense and I think because the arbitrary 25%/75% bounds are basically doing what Kahneman recommends in “Thinking Fast and Slow”. For extremities you develop an equation to explain the new level of performance and then you attribute half of the actual performance to the new skill level and half to luck. For this analysis, that would apply by taking a blended average of your new equation to explain rest of season stats, averaged with the original preseason expectation of that team. That would naturally raise up high end expectations of “underperformers” (Nats) to date and cap the possible performance of “over performers” (Twins) without artificially putting too tight of a bound on middle of the road teams (Mets), I think.
If you are a Met fan, I don’t know how you could have watched the past two months of the season and arrive at the conclusion that an 84 win ceiling is unreasonable.
I think that the post-Memorial Day predictions have a low r-squared because of the trade deadline and expanded roster periods where teams start to throw in the towel or make moves to strengthen their rosters.
I would be interested on the analysis of Memorial Day wpct on the wpct in June-July where the rosters would be largely the same with the exception of injured players, and you’d think that most teams have to deal with minor injuries throughout the season.
All in all these articles support something I’ve been saying for a while – the season is too long. By the middle of May you know who is good, who is bad, and who is mediocre.
Rather than re-inventing (with less accuracy) the FG rest of season projections algo, I would suggest looking at why we are seeing the highest HR rate in the history of baseball. Is it random variance, a talent imbalance, or MLB tweaking the ball yet again?
Are you talking about standings at the START of Memorial Day — as I peruse my newspaper this very moment — or at the END? I don’t see that specified in this piece or the prior one.
This may seem a nitpick, but it’s axiomatic in math and science to DEFINE THE TERMS as clearly as possible. And given the large variance in % of schedule already baked into “Memorial Day standings” — plus the discussion of 2019 records well before Memorial Day — any unnecessary vagueness is really unfortunate.
Research such as this should be independently verifiable. But I can’t check this work, because basic terms aren’t defined.
Fwiw, in the study period, teams played anywhere from 41 to 55 games before Memorial Day — 25% to 34% of their schedule. Such a big spread weakens the predictive significance of this study. If you studied records strictly as of those endpoints of schedule %, you’d get very different findings.
And the quick pass at gauging playoff likelihood from standings before Memorial Day is a bridge too far, and is addressed poorly here. The fact that 66% of final playoff teams in the past 7 years held a berth as of Memorial Day does not suggest that we can simply expect 6 or 7 of this year’s final playoff teams to be holding a spot right now, as was stated. That’s just not how those data work. The question of playoff implications should be handled separately, not as a brief interlude in a study focused on records.
One thing I wonder is how strength of schedule affects expected w/l record. For example, the Yankees have played the Orioles a ton of times this season, as well as the Royals and White Sox. I would expect them to underperform their expected w/l record for the rest of the season, since they will play the stronger teams more often. Did the outliers in over/under performance vs expected w/l play more weaker/stronger teams prior to Memorial Day?