The 2018 Mets finished the season strong, going 18-10 (.643) in the month of September. That was the sixth-best record in the majors and four of the five teams that finished ahead of them went to the playoffs. Even the non-playoff team was the Rays, who finished the year with 90 wins. A lot of people prefer to dismiss numbers put up in September, saying that they’re meaningless. They hold this opinion for a variety of reasons. But let’s put some recent real time results to the test, to see on a team-wide basis if September records had any predictive qualities for the following season.
What follows is a three-year sample for the years 2015-2017, giving us 90 teams to examine. In the chart, the columns labeled “W” and “L” and “W-L%” are what the clubs did in the month of September. The “Overall” column is their full-season winning percentage and “Diff” is the difference between what they did in September compared to what they did for the full season. The chart is sorted from greatest positive to greatest negative difference. The last two columns are the winning percentage for the following season and the difference in winning percentage from our initial year to the following season.
Year | Team | W | L | W-L% | Overall | Diff | W% (Y+1) | Year over Year |
---|---|---|---|---|---|---|---|---|
2017 | CLE | 26 | 4 | .867 | .630 | .237 | .562 | -.068 |
2016 | ATL | 18 | 10 | .643 | .422 | .221 | .444 | .022 |
2015 | MIA | 18 | 12 | .600 | .438 | .162 | .491 | .053 |
2017 | PHI | 16 | 13 | .552 | .407 | .145 | .494 | .087 |
2017 | NYY | 20 | 9 | .690 | .562 | .128 | .617 | .055 |
2017 | OAK | 17 | 12 | .586 | .463 | .123 | .599 | .136 |
2015 | CHC | 23 | 9 | .719 | .599 | .120 | .640 | .041 |
2015 | LAA | 20 | 11 | .645 | .525 | .120 | .457 | -.068 |
2016 | MIL | 16 | 13 | .552 | .451 | .101 | .531 | .080 |
2017 | HOU | 21 | 8 | .724 | .623 | .101 | .636 | .013 |
2016 | SEA | 18 | 11 | .621 | .531 | .090 | .481 | -.050 |
2017 | CHC | 19 | 10 | .655 | .568 | .087 | .583 | .015 |
2017 | CHW | 15 | 15 | .500 | .414 | .086 | .383 | -.031 |
2016 | NYM | 18 | 11 | .621 | .537 | .084 | .432 | -.105 |
2015 | TEX | 20 | 12 | .625 | .543 | .082 | .586 | .043 |
2016 | BOS | 19 | 10 | .655 | .574 | .081 | .574 | .000 |
2015 | BAL | 18 | 13 | .581 | .500 | .081 | .549 | .049 |
2015 | BOS | 17 | 14 | .548 | .481 | .067 | .574 | .093 |
2016 | LAA | 15 | 14 | .517 | .457 | .060 | .494 | .037 |
2015 | CLE | 17 | 14 | .548 | .503 | .045 | .584 | .081 |
2017 | STL | 16 | 13 | .552 | .512 | .040 | .543 | .031 |
2017 | MIL | 16 | 12 | .571 | .531 | .040 | .589 | .058 |
2015 | TOR | 19 | 12 | .613 | .574 | .039 | .549 | -.025 |
2016 | CLE | 18 | 11 | .621 | .584 | .037 | .630 | .046 |
2016 | BAL | 17 | 12 | .586 | .549 | .037 | .463 | -.086 |
2015 | COL | 15 | 18 | .455 | .420 | .035 | .463 | .043 |
2017 | BOS | 17 | 11 | .607 | .574 | .033 | .667 | .093 |
2017 | ARI | 17 | 11 | .607 | .574 | .033 | .506 | -.068 |
2017 | TOR | 14 | 14 | .500 | .469 | .031 | .451 | -.018 |
2015 | SEA | 15 | 15 | .500 | .469 | .031 | .531 | .062 |
2015 | ARI | 16 | 15 | .516 | .488 | .028 | .426 | -.062 |
2017 | SFG | 11 | 15 | .423 | .395 | .028 | .451 | .056 |
2015 | LAD | 19 | 13 | .594 | .568 | .026 | .562 | -.006 |
2016 | LAD | 17 | 12 | .586 | .562 | .024 | .642 | .080 |
2016 | ARI | 13 | 16 | .448 | .426 | .022 | .574 | .148 |
2015 | WSN | 17 | 15 | .531 | .512 | .019 | .586 | .074 |
2016 | CHW | 15 | 15 | .500 | .481 | .019 | .414 | -.067 |
2016 | SDP | 13 | 17 | .433 | .420 | .013 | .438 | .018 |
2016 | CIN | 13 | 17 | .433 | .420 | .013 | .420 | .000 |
2017 | KCR | 15 | 15 | .500 | .494 | .006 | .358 | -.136 |
2015 | ATL | 13 | 18 | .419 | .414 | .005 | .422 | .008 |
2016 | STL | 16 | 14 | .533 | .531 | .002 | .512 | -.019 |
2016 | WSN | 17 | 12 | .586 | .586 | .000 | .599 | .013 |
2015 | NYM | 17 | 14 | .548 | .556 | -.008 | .537 | -.019 |
2017 | MIN | 15 | 14 | .517 | .525 | -.008 | .481 | -.044 |
2015 | DET | 14 | 17 | .452 | .460 | -.008 | .534 | .074 |
2015 | TBR | 15 | 16 | .484 | .494 | -.010 | .420 | -.074 |
2017 | ATL | 13 | 17 | .433 | .444 | -.011 | .556 | .112 |
2015 | MIN | 16 | 16 | .500 | .512 | -.012 | .364 | -.148 |
2016 | OAK | 12 | 17 | .414 | .426 | -.012 | .463 | .037 |
2017 | TBR | 13 | 14 | .481 | .494 | -.013 | .556 | .062 |
2015 | CHW | 15 | 18 | .455 | .469 | -.014 | .481 | .012 |
2015 | MIL | 13 | 19 | .406 | .420 | -.014 | .451 | .031 |
2017 | NYM | 12 | 17 | .414 | .432 | -.018 | .475 | .043 |
2016 | CHC | 18 | 11 | .621 | .640 | -.019 | .568 | -.072 |
2016 | NYY | 15 | 15 | .500 | .519 | -.019 | .562 | .043 |
2016 | MIN | 10 | 19 | .345 | .364 | -.019 | .525 | .161 |
2016 | TBR | 12 | 18 | .400 | .420 | -.020 | .494 | .074 |
2017 | COL | 15 | 14 | .517 | .537 | -.020 | .558 | .021 |
2015 | PHI | 11 | 19 | .367 | .389 | -.022 | .438 | .049 |
2017 | SDP | 12 | 17 | .414 | .438 | -.024 | .407 | -.031 |
2017 | CIN | 11 | 17 | .393 | .420 | -.027 | .414 | -.006 |
2015 | PIT | 19 | 14 | .576 | .605 | -.029 | .484 | -.121 |
2016 | DET | 14 | 14 | .500 | .534 | -.034 | .395 | -.139 |
2017 | PIT | 12 | 16 | .429 | .463 | -.034 | .509 | .046 |
2015 | SFG | 15 | 16 | .484 | .519 | -.035 | .537 | .018 |
2016 | SFG | 15 | 15 | .500 | .537 | -.037 | .395 | -.142 |
2017 | WSN | 16 | 13 | .552 | .599 | -.047 | .506 | -.093 |
2016 | TEX | 15 | 13 | .536 | .586 | -.050 | .481 | -.105 |
2017 | SEA | 12 | 16 | .429 | .481 | -.052 | .549 | .068 |
2016 | PHI | 11 | 18 | .379 | .438 | -.059 | .407 | -.031 |
2016 | MIA | 12 | 16 | .429 | .491 | -.062 | .475 | -.016 |
2017 | TEX | 12 | 17 | .414 | .481 | -.067 | .414 | -.067 |
2015 | NYY | 15 | 17 | .469 | .537 | -.068 | .519 | -.018 |
2016 | HOU | 13 | 16 | .448 | .519 | -.071 | .623 | .104 |
2015 | CIN | 10 | 22 | .313 | .395 | -.082 | .420 | .025 |
2016 | COL | 11 | 18 | .379 | .463 | -.084 | .537 | .074 |
2016 | KCR | 12 | 17 | .414 | .500 | -.086 | .494 | -.006 |
2015 | OAK | 10 | 20 | .333 | .420 | -.087 | .426 | .006 |
2017 | MIA | 11 | 18 | .379 | .475 | -.096 | .391 | -.084 |
2015 | HOU | 13 | 17 | .433 | .531 | -.098 | .519 | -.012 |
2017 | LAA | 11 | 17 | .393 | .494 | -.101 | .494 | .000 |
2016 | TOR | 13 | 16 | .448 | .549 | -.101 | .469 | -.080 |
2015 | KCR | 15 | 17 | .469 | .586 | -.117 | .500 | -.086 |
2016 | PIT | 11 | 19 | .367 | .484 | -.117 | .463 | -.021 |
2015 | STL | 15 | 16 | .484 | .617 | -.133 | .531 | -.086 |
2015 | SDP | 10 | 21 | .323 | .457 | -.134 | .420 | -.037 |
2017 | DET | 6 | 24 | .200 | .395 | -.195 | .395 | .000 |
2017 | LAD | 13 | 17 | .433 | .642 | -.209 | .564 | -.078 |
2017 | BAL | 7 | 21 | .250 | .463 | -.213 | .290 | -.173 |
So, looking at the info from the first row, the 2017 Indians went 26-4 in September, for an .867 winning percentage in the final month of the season. Overall in 2017, they had a .630 winning percentage. Their difference of .237 from September to their year-long winning percentage was the greatest of all 90 teams in our sample. The following year, 2018, the Indians had a .562 winning percentage. While that was good for 91 wins, it was a fall from what Cleveland did in 2017. So what the Indians did in September of 2017 was not necessarily a harbinger of things to come in 2018.
But eight of the next nine teams with the best difference between their September record and their overall record saw their season-long winning percentage go up in the following year. Our top 10 in positive September differential run the gamut, from a .407 winning percentage (66 wins) to a .630 winning percentage (102 wins).
If we expand to the 20-best teams in September differential, we see that 15 of them improved their winning percentage the following season.
Perhaps just as importantly, we see that teams that put up the worst September differential generally performed worse the following year. Eight of the bottom 10 teams in September differential saw their winning percentage go down in the following season. And when we expand to the bottom 20 teams, we see that 14 of them turned in a worse winning percentage the next year.
Of the middle 50 teams by September differential, 29 of them saw an increase in winning percentage the following season compared to 21 that saw their winning percentage go down.
So, the top 10 teams saw their winning percentage increase 80% of the time the following season
The top 20 teams saw their winning percentage increase 75% of the time
The middle 50 teams saw their winning percentage increase 58% of the time
The bottom 20 teams saw their winning percentage increase 30% of the time
The bottom 10 teams saw their winning percentage increase 20% of the time
No doubt that there is likely a more sophisticated way to look at the data. And that more sophisticated way may show something entirely different. But this first glance seems to indicate both a fairly strong correlation between teams that played significantly better in September than they did overall increasing their winning percentage the following season, as well as teams that played significantly worse in September showing a decrease in winning percentage the next year.
The 2018 Mets had a September differential of .168, which would be the third-best mark if eligible for our 2015-17 sample. Another thing our sample shows is that seven of the eight teams that finished with an overall winning percentage under .500 that had a winning record in September had an overall increase in winning percentage the following season. Only the 2015 Diamondbacks, who had an overall winning percentage of .488 and a September mark of .516, saw their fortunes decline the following year.
Of course, we should also note that the 2016 Mets had a .084 September differential, the 14th-best mark in our sample, and they saw their following season winning percentage decline by .105 – one of just five clubs in the top 20 to go backwards and the largest faller of the five.
I think September games have significance in a lot of cases. With so many postseason berths up for grabs, many games involve one or both teams in the hunt. then there are marginal players looking to impress for the following season, not to mention players looking to pad their stats for contract negotiations
I plotted ‘Diff’ as the X-variable and ‘Year over Year’ as the Y-variable in a regression and got a correlation value of .09 – which indicates very weak correlation. I cant post the the graph here, but looking at it visually one would conclude no correlation.
I also did a regression on Sep record vs next year’s record. Correlation was slighty stronger at 0.30, but the standard deviation was .06 – which is 10 wins, and makes the regression equation useless.
There’s just too many factors going on to develop a theory solely based on september records.
Was the regression for all 90 teams? What would it be for just the top 10 and bottom 10 teams?
Very interesting. Perhaps another baseball myth identified by BJ. Back in September, I actually thought about what the strong Met finish actually meant going forward, looking for resaons for optimism. Being lazy, I didn’t do much if any research, but it did appear that the Mets played well not only against teams that were out of it, but also against the teams that needed to win. I had the Mets at 7-5 vs teams in the race (excluding the Red Sox on cruise control). Looking back to August, I had them at 6-7, and post all-star July 1-1. In all, in the 2nd half, they were 14-13. That’s pretty good ball against good to decent teams playing to win. I also give Callaway some credit, as the team certainly didn’t mail it in, and even was quite fiesty vs the better competition.
I sort of got frustrated with their winning because that kept dropping them lower and lower in next year’s amateur draft.
While there are many factors that go into one team’s roster as compared to the next, in the other major sports often we do see a strong second half point towards a better next year. Will that be playoffs, is the magic question. I, too, was glad to see the Mets show heart until the end. Especially since most of the team is returning, to have a bad finish may have meant a new voice in the clubhouse because had they not shown a pulse, BVW would have had to bring in Girardi. Where I didn’t see the team finish strong was the bullpen. No one stepped up even at year’s end, signaling a major overhaul needed.
Speaking of Girardi, while we hear Riggleman for bench coach because of his solid strategy, would Girardi take this job?
I cannot imagine Joe Girardi leaving Home to be Bench Coach for the Mets.
Given the underlying Macro Metrics with which teams approach the game and the lack of situational adjustments that are made, is the game lacking young coaches capable of the in-inning minutia and game strategy that a Bench Coach Provides? Is there some indication that only some “Old Gray Steady Hand” can identify the “ancient ways” of double switches, etc.