If you’ve read some of my previous stuff around the internet you likely know that I enjoy using simple simulations to do analysis. With the regular season upon us, I thought it would be interesting to run one such simulation before each series to serve as our PAH9 version of a series preview.

First then a quick overview of the simulation. It is built similar to the one that I described at Fangraphs

In order to run the simulation needs the [...] teams true talent win percentage. The simulation is a simple Monte Carlo that determines the winner of each game using random draws bounced up against log5 based winning percentages. For example, if we want to simulate the outcome of a game between Team A that has a 0.600 true talent win percentage and Team B that has a 0.450 win percentage, we first calculate the probability that A beats B using the log5 equation linked above. That calculation says that Team A should have a 0.647 winning percentage against Team B. To simulate a game between these teams then, the simulation draws a random number between 0 and 1 and if the number is less than or equal to 0.647 then Team A wins, otherwise Team B wins.

So, how did I derive the true talent win percentages? The process is similar to the one used by JinAZ over at BtB to do his power ranking, only I use projections instead of results (as the season continues I’ll probably use updated projections). To approximate runs scored I take the eight starting position players and calculate the projected runs/game for that lineup by plugging each player’s CHONE projection into the Runs Created formula that Fangraphs uses, and scaling that to a single game. To approximate runs allowed I take the (starter’s projected ERA/.92)*IP/start+(bullpen projected ERA/.92)*(9-IP/start) and from that I subtract the team’s defensive runs saved per game. These two calculations are plugged into PythagenPat to find the each team’s true talent win% with the respective lineup/SP combinations. On top of that %, I add a 0.040 home field advantage for the home team. Plug those into the simulation and you get the following for the Cards D-Backs series

The chart is Cards wins across the x-axis and frequency on the y-axis, so you can see that the simulation says that Cards should sweep about 18% of the time. The most common set of events was the Cards winning the first and third games, and losing the middle game to Dan Haren. This series of events happened ~20% of the time.

For the curious, here’s the table of true talent win % (with home field advantage) I derived

Game 1 | Game 2 | Game 3 | |
---|---|---|---|

Cards | 0.587 | 0.584 | 0.676 |

D-Backs | 0.5 | 0.606 | 0.533 |

So there ya have it… the Cards should win 2 of 3…. Again (well 40% of the time anyhow).

For any of you team specific bloggers that stumble upon this, if you want a copy of the simulation (it’s in excel using vba) just drop me a line and I can get you a copy (after I pretty it up a little and add a GUI like function).

I love it when stat nerds share their toys. You have an email in your inbox.

Steve, this is really well done. I’m very interested in the file you have, but am unsure of how to contact you. You can send me an email or if you let me know how I can contact you, I’ll do so soon. Thanks for the great work.

Hey mb21,

Erik mentioned in his email that you’d be interested / had been discussing this. I’ll email both of you in the next day or so (I have to clean it up a little so it’s a little more self-explanatory).

My methodology for getting to true talent win % is VERY close to what you’re doing (Erik shared your methodology), and I actually want to incorporate at least one thing of yours that I’m doing different.

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