Ottoneu Keeper and Inflation Data
Last year I posted an article summarizing the keeper results for all ottoneu FanGraphs points leagues, and now that the ottoneu keeper deadline has come and gone once again I am back with an update.
For context, here were the keeper results for 2016:
| League Age | Proj 1st | # Keepers | Salary Kept | Value Kept | Surplus | Inflation |
|---|---|---|---|---|---|---|
| Sixth Year Leagues | $549 | 294 | $3,239 | $3,553 | $314 | 30.8% |
| Fifth Year Leagues | $546 | 296 | $3,293 | $3,530 | $236 | 23.0% |
| Fourth Year Leagues | $535 | 301 | $3,288 | $3,451 | $163 | 14.2% |
| Third Year Leagues | $537 | 288 | $3,237 | $3,466 | $229 | 21.2% |
| Second Year Leagues | $507 | 271 | $3,173 | $3,197 | $25 | 1.0% |
| Grand Average | $536 | 291 | $3,252 | $3,453 | $200 | 18.8% |
- In 2016 leagues kept 291 players on average, costing $3,252 and worth $3,453 (according to my personal values), resulting in 18.8% inflation
- Those per team averages come out to about 24 players kept per team with $271 in salary and $288 in value
- There was an unexplained inflation “valley” in the fourth year leagues, I suspected at the time that this was just a sample size quirk
- My general conclusion was that as leagues get older the best teams in a league get better and inflation rises, with a sharp jump from second year to third year leagues
Here are the results for 2017 (please keep in mind that I’ve excluded any league that had teams over the $400/40 caps at the keeper deadline, leagues with less than 12 teams, and vote-off leagues):
| League Age | Proj 1st | # Keepers | Salary Kept | Value Kept | Surplus | Inflation |
|---|---|---|---|---|---|---|
| Seventh Year Leagues | $522 | 307 | $3,383 | $3,568 | $185 | 25.3% |
| Sixth Year Leagues | $525 | 309 | $3,466 | $3,585 | $119 | 18.2% |
| Fifth Year Leagues | $523 | 300 | $3,371 | $3,503 | $132 | 17.9% |
| Fourth Year Leagues | $499 | 303 | $3,362 | $3,497 | $135 | 17.6% |
| Third Year Leagues | $494 | 290 | $3,396 | $3,459 | $63 | 11.4% |
| Second Year Leagues | $490 | 275 | $3,238 | $3,168 | ($71) | -1.0% |
| Grand Average | $512 | 300 | $3,381 | $3,488 | $107 | 16.3% |
- In 2017 leagues kept 300 players on average, costing $3,381 and worth $3,488 (according to my personal values), resulting in 16.3% inflation
- Those per team averages come out to about 25 players kept per team with $282 in salary and $291 in value
- In general teams kept one more player than they did last year, spending $11 more but with only $3 more in value
- There is no inflation “valley” in 2017, as inflation is higher for every older league group
- Inflation overall is down a bit from 2016, which is mostly explained by leagues/teams keeping more players than they did last year
Lastly, now that I have two seasons worth of data I thought it would be instructive to combine the results:
| League Age | Proj 1st | # Keepers | Salary Kept | Value Kept | Surplus | Inflation |
|---|---|---|---|---|---|---|
| Seventh Year Leagues | $522 | 307 | $3,383 | $3,568 | $185 | 25.3% |
| Sixth Year Leagues | $537 | 302 | $3,353 | $3,569 | $217 | 24.5% |
| Fifth Year Leagues | $535 | 298 | $3,332 | $3,517 | $184 | 20.5% |
| Fourth Year Leagues | $517 | 302 | $3,325 | $3,474 | $149 | 15.9% |
| Third Year Leagues | $516 | 289 | $3,317 | $3,463 | $146 | 16.3% |
| Second Year Leagues | $499 | 273 | $3,206 | $3,183 | ($23) | 0.0% |
| Grand Average | $524 | 296 | $3,317 | $3,471 | $154 | 17.6% |
- In general, second year leagues have virtually no inflation, but inflation rises to about 16% for third and fourth year leagues, 20% for fifth year leagues, and about 25% for sixth and seventh year leagues
- I suspect that inflation plateaus around the seven year mark, and does not keep increasing every year, but even with two seasons worth of data it’s hard to be sure
Any questions about these results? Were there any surprising keeps or cuts in your leagues yesterday? Let me know in the comments!
Justin is a life long Cubs fan who has been playing fantasy baseball for 20+ years, and an ottoneu addict since 2012. Follow him on Twitter @justinvibber.
Good work Justin. Would be nice to see sample size and some measures of dispersion given averaging across leagues (and now years). Maybe merits an article unto itself?