The Math of Winning Ottoneu (2016)

by
October 26, 2016

With 2016 leagues in the books, I’d like to present some league-wide season-ending stats to see what kind of conclusions we can draw about success in the very data-driven game of Ottoneu.  The focus here is one of the more popular scoring formats, FanGraphs Points (FGPTS), and the numbers you see in each of the first two charts represent the average standings data for all teams/all leagues by final 2016 finish.

For example, the average score of all 1st place teams in 2016 was 19,227 points.

2016 FGPTS by Place
Place FGPTS IP AB GP Teams 1,450 IP IP % 1,800 GP GP % FGPTS PTS Gain
1st 19,227 1,490 7,285 1,923 111 103 93% 111 100% 19,325 98
2nd 18,568 1,484 7,206 1,914 111 96 86% 109 98% 18,672 104
3rd 17,978 1,464 7,140 1,905 111 80 72% 107 96% 18,151 173
4th 17,475 1,438 7,075 1,891 111 64 58% 107 96% 17,673 198
5th 17,044 1,422 6,949 1,867 111 50 45% 93 84% 17,311 267
6th 16,597 1,398 6,859 1,847 111 41 37% 87 78% 17,049 452
7th 16,109 1,363 6,757 1,829 111 22 20% 75 68% 16,567 458
8th 15,671 1,346 6,646 1,802 111 20 18% 58 52% 16,354 683
9th 15,177 1,278 6,455 1,762 111 8 7% 39 35% 16,151 974
10th 14,671 1,263 6,358 1,733 111 7 6% 30 27% 15,655 984
11th 13,820 1,180 6,131 1,687 111 2 2% 19 17% 15,528 1,708
12th 12,290 1,060 5,538 1,551 111 0 0% 8 7% – –
Numbers represent averages by final placement in the standings across all FGPTS leagues.

First, some observations about 2016:

  • It is obvious, but teams that play the most, score the most.
    • In other words, as you learn the game of Ottoneu you quickly see that the most successful teams do everything they can to hit the 1,944 Games Played and 1,500 Innings Pitched caps.
    • The average IP of all 1st place teams was nearly 1,500 (1,490), so the 2017 takeaway is to make hitting the GP and IP a part of your strategy, not just an ancillary benefit of a well-built roster.  This could mean owning fewer prospects, platooning hitters more frequently and effectively, paying a few dollars more for notable innings-eaters like Max Scherzer, David Price, or Corey Kluber, or reevaluating workhorse veterans like Ian Kennedy, Adam Wainwright, or Hisashi Iwakuma.
  • It appears more difficult for teams to reach the innings cap than the games cap, so you may want to carry more starting pitchers than you initially plan to roster.
    • Case in point: League 100 has adopted a very creative incentive structure for the 2017 season that will require their owners to be ultra-active (minimum 1,450 IP and 1,800 GP) in managing their innings and games if they are to reap the benefits.  As you can see, 84% of 5th place teams hit at least 1,800 GP in 2016, but only 45% of 5th place teams hit the 1,450 IP mark.
    • Using the League 100 thresholds as a proxy for rostering to maximize lineup requirements, the far right “PTS Gain” shows you how many additional points a team hitting at least 1,450 IP and 1,800 GP would score beyond the average of all teams, by placement.  Again, the extra 256 points a 5th place team could score over the average by just targeting these thresholds could be the different between 5th place and 3rd, or even 2nd place, in a tight race.
2015 FGPTS by Place
Place FGPTS IP AB GP Teams 1,450 IP IP % 1,800 GP GP % FGPTS PTS Gain
1st 18,784 1,498 7,173 1,912 95 90 95% 94 99% 18,800 16
2nd 18,092 1,491 7,072 1,895 95 84 88% 92 97% 18,137 45
3rd 17,482 1,479 7,020 1,882 95 81 85% 88 93% 17,559 77
4th 17,092 1,459 6,902 1,858 95 68 72% 78 82% 17,287 195
5th 16,564 1,443 6,843 1,845 95 53 56% 75 79% 16,820 256
6th 16,098 1,416 6,733 1,827 95 42 44% 64 67% 16,459 361
7th 15,688 6,534 1,367 1,780 95 22 23% 48 51% 16,141 454
8th 15,258 1,358 6,534 1,776 95 26 27% 35 37% 15,834 576
9th 14,873 1,314 6,398 1,752 95 12 13% 33 35% 15,699 826
10th 14,242 1,275 6,208 1,703 95 11 12% 18 19% 15,366 1,124
11th 13,619 1,224 6,046 1,659 95 4 4% 10 11% 14,406 787
12th 12,220 1,078 5,583 1,546 95 1 1% 4 4% – –
Numbers represent averages by final placement in the standings across all FGPTS leagues.

Just for comparison, the IP and GP trends (and deficiencies) are similar for 2015, too.

And just for fun, I’ve converted the average standings data by place into more traditional stats, essentially showing the offense of the average 1st place team to be something like 2016 Neil Walker.

2016 FGPTS Stats by Place
2016 AVG OBP SLG wOBA WHIP K/9 FIP SV + HLD P/G P/IP H PTS P PTS
1st 0.277 0.352 0.477 0.358 1.21 9.33 3.45 176 5.78 5.44 11,116 8,111
2nd 0.274 0.348 0.468 0.352 1.23 9.23 3.55 173 5.58 5.32 10,675 7,893
3rd 0.272 0.346 0.465 0.350 1.25 8.98 3.64 159 5.50 5.13 10,472 7,507
4th 0.270 0.343 0.460 0.347 1.26 8.97 3.67 154 5.38 5.08 10,173 7,302
5th 0.269 0.342 0.456 0.346 1.26 8.90 3.71 148 5.30 5.03 9,900 7,144
6th 0.269 0.341 0.455 0.344 1.27 8.78 3.77 147 5.24 4.96 9,680 6,917
7th 0.268 0.339 0.453 0.343 1.28 8.67 3.82 140 5.19 4.86 9,490 6,620
8th 0.267 0.339 0.448 0.341 1.29 8.68 3.86 133 5.12 4.81 9,217 6,454
9th 0.268 0.340 0.452 0.343 1.29 8.75 3.87 131 5.14 4.80 9,057 6,120
10th 0.266 0.336 0.446 0.339 1.29 8.69 3.90 121 5.02 4.74 8,699 5,972
11th 0.265 0.336 0.444 0.338 1.31 8.54 3.96 108 4.97 4.63 8,374 5,447
12th 0.264 0.334 0.444 0.337 1.31 8.57 3.94 103 4.74 4.53 7,358 4,932

 

2015 FGPTS Stats by Place
2015 AVG OBP SLG wOBA WHIP K/9 FIP SV + HLD P/G P/IP H PTS P PTS
1st 0.273 0.346 0.457 0.348 1.17 9.15 3.23 167 5.40 5.65 10,322 8,462
2nd 0.269 0.342 0.448 0.342 1.19 8.97 3.35 168 5.21 5.51 9,880 8,212
3rd 0.269 0.339 0.445 0.340 1.22 8.71 3.45 153 5.12 5.31 9,637 7,845
4th 0.269 0.340 0.442 0.339 1.23 8.70 3.48 151 5.08 5.25 9,435 7,657
5th 0.267 0.335 0.434 0.334 1.23 8.61 3.54 148 4.92 5.20 9,071 7,493
6th 0.267 0.335 0.433 0.334 1.25 8.47 3.61 140 4.88 5.08 8,917 7,181
7th 0.266 0.335 0.434 0.334 1.24 8.46 3.57 137 4.88 5.13 8,676 7,012
8th 0.266 0.333 0.431 0.332 1.27 8.30 3.69 134 4.80 4.97 8,525 6,733
9th 0.266 0.334 0.431 0.332 1.26 8.30 3.68 126 4.78 4.96 8,372 6,501
10th 0.263 0.331 0.427 0.329 1.27 8.24 3.71 126 4.69 4.93 7,983 6,259
11th 0.263 0.329 0.422 0.327 1.27 8.21 3.72 115 4.61 4.90 7,644 5,975
12th 0.264 0.330 0.422 0.327 1.28 8.11 3.79 89 4.58 4.75 7,097 5,123

Some final notes:

  • Offense was definitely spiked in 2016, with the average wOBA up nearly 3% over the previous for almost ever place in the standings.
  • The average P/G for all leagues in 2016 was up as well (5.25) compared to 2015 (4.92).
  • SLG in 2016 was .456 vs. .436 in 2015.
  • Unsurprisingly, pitching came down a bit in 2016 as the offense increased.  A nearly inverse trend to the offense is visible year over year as the average P/IP for all leagues was 4.94 in 2016 compared to 5.14 in 2015.
  • Always big factor for linear-weights: HRA/9 was up for pitchers just over 10% in 2016 (1.09) vs. 2015 (0.93).

Also: The Math of Winning Ottoneu (2015)





 

Trey is a 20+ year fantasy veteran and an early adopter of Ottoneu fantasy sports. He currently administers the Ottoneu community, a network of ~1,200 fantasy baseball and football fans talking sports daily. More resources here: http://community.ottoneu.com

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David
8 years ago

Can’t disagree that you need to fill your limits to win. But there are other correlations here. teams that aren’t doing well lose interest and change rosters less frequently, and teams with lost of injuries tend to do less well and of course have more trouble filling their innings and game limits.

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