Yesterday, I asked how many wins a team full of recent draft picks and replacement-level NFL players would fare. I don’t think there’s a right answer to the question, but it might be a more important question than you think (and you’ll see why on Monday). But I have at least one way we can try to estimate how many games such a team would win.
Neil once explained how you can project a team’s probability of winning a game based on the Vegas pre-game spread. We can use the SRS to estimate a point spread, and if we know the SRS of our Replacement Team, we can then figure out how many projected wins such a team would have. How do we do that?
First, we need to come up with a mythical schedule. I calculated the average SRS rating (after adjusting for home field) of the best, second best, third best… and sixteenth best opponents for each team in the NFL from 2004 to 2011. The table below shows the “average” schedule for an average team:
| Opp Rk | Opp Strength |
|---|---|
| 1 | 12.4 |
| 2 | 9.3 |
| 3 | 7.2 |
| 4 | 5.7 |
| 5 | 4.4 |
| 6 | 3.2 |
| 7 | 2 |
| 8 | 0.6 |
| 9 | -0.6 |
| 10 | -1.7 |
| 11 | -2.9 |
| 12 | -4.3 |
| 13 | -5.7 |
| 14 | -7.4 |
| 15 | -9.4 |
| 16 | -12.6 |
On average, the best team a typical team will face in a season (after factoring in home field) will be 12.4 points above average, while the worst will be 12.6 points below average. Once we know the SRS rating of each opponent, if we have a projected SRS rating for our Replacement team, we can use Neil’s formulate to calculate the expected winning percentage in each game. If you sum the sixteen expected winning percentages, you get the number of expected wins.
I installed a new plug-in that is an interactive calculator. There are some deficiencies when using the calculator for this purpose [1]Neil’s formula is an advanced formula that uses the normal distribution; the interactive calculator does not have such capabilities, but I was able to use a best-fit exponential formula. The … Continue reading, but in general I think this is pretty neat. The calculator is dynamic, so if you change any one value, all the other values will immediately change.
If our hypothetical team is 10 points below average, they should be expected to win 4.1 games. Drop them to -13.7, and they’re projected to win just 3.0 games. You can change any of the input values (SRS rating of replacement team, SRS of any opponent) below:
Obviously figuring out the SRS rating isn’t easy, and maybe this just kicks the can down the road. The Chiefs had an SRS of -14.0 last year, while the Jaguars were at -13.0. But those SRS ratings don’t represent the “true” values of the Chiefs or Jaguars, either. Those SRS ratings include all the bad luck and other things that happened to Kansas City and Jacksonville last year. The -14.0 for Kansas City tells us their rating when they finish the year -24 in the turnover battle, but if we simulated the 2012 season 10,000 times, the Chiefs would have a much better turnover ratio in nearly every season. That would cause their SRS to rise significantly.
On the other hand, the Chiefs (and the Jags) are more talented than a mythical team that consists of three first rounders, three second rounders, three third rounders, and 44 late round picks or veterans minimum-type players.
I think I’m settling in on our mythical replacement team finishing 3-13, but am open to other thoughts. More on why this is important on Monday.
References
| ↑1 | Neil’s formula is an advanced formula that uses the normal distribution; the interactive calculator does not have such capabilities, but I was able to use a best-fit exponential formula. The formula is not the same, so there will be errors as you change the SRS rating farther from -10, but for most values, it will get you close enough to be reasonable. |
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