A three-year look at September records and following year performance

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.

7 comments for “A three-year look at September records and following year performance

  1. John Fox
    November 4, 2018 at 5:10 pm

    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

  2. Name
    November 4, 2018 at 5:13 pm

    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.

    • November 4, 2018 at 5:52 pm

      Was the regression for all 90 teams? What would it be for just the top 10 and bottom 10 teams?

  3. TJ
    November 4, 2018 at 5:21 pm

    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.

  4. JImO
    November 4, 2018 at 5:50 pm

    I sort of got frustrated with their winning because that kept dropping them lower and lower in next year’s amateur draft.

  5. TexasGusCC
    November 5, 2018 at 12:54 am

    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?

  6. November 5, 2018 at 8:03 am

    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.

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