OOTP Developments Forums - View Single Post

Garlon · 07-01-2019, 12:47 PM

We do not have this LHB/RHB data for parks, if we had the data then I'd have put that into the file. What we do have for all parks going all the way to 1871 in the Runs Scored per game at home and on the road for both the home team and their opponents. From this data and with the use of the quadratic formula the intent was to recreate the scoring environment for the parks.

We now have more discrete data for ballparks going back to 1906. So in the file I posted above, if you want the relative BA/2B/3B/HR factors for parks, which in theory will also produce the same scoring environment you can use that one as well.

You can set every park to 1.05 for HR and they will all actually play like 1.000 if you do this. You can invent your own ratings if you want. Where are you obtaining the data? How is it being formatted for use in OOTP?

Any impact that the wind and temperature had on the ballparks historically should have been accounted for in the stats and hence in the factors. Now, in OOTP here is also a weather file. I use a custom file because I believe that the weather file actually functions as another factor in the game.

As a rule of thumb, if you were to set BA/2B/3B/HR all to 1.05, or 5% above average, expect your scoring to go up at least double that, so more than 10%. So if you use edited factors you may not recreate the proper scoring environment if the factors are not correct for the game.

I think in general people make too much out of the park factors. You have to play half of your games on the road anyway, you can't have a home version and an away version of your team.

Let's consider team A whose park yields 20% more HR and let's consider a player on team B who hits 40 HR playing in a completely average ballpark who then gets moved to team A. Well, in league with 15 teams, those HR factors should add up to 15.000. Team A has a park with 1.200 for HR, that means the other 14 parks must equal the other 13.80. Ok, 13.80/14 = .985. This player now plays half of his games in 1.200 and half in .985. OK, so 1.200+.985 = 2.185 and 2.185/2 = 1.092. So how many HRs does this guy now expect to have this season? 1.092*40 = 43.68. So about 3 more HRs for the season. If he is a guy who hits 9 HRs per year, he might be expected to hit 10 with team A.

07-01-2019, 12:47 PM	#11
Garlon Hall Of Famer Join Date: Jun 2004 Posts: 3,754	We do not have this LHB/RHB data for parks, if we had the data then I'd have put that into the file. What we do have for all parks going all the way to 1871 in the Runs Scored per game at home and on the road for both the home team and their opponents. From this data and with the use of the quadratic formula the intent was to recreate the scoring environment for the parks. We now have more discrete data for ballparks going back to 1906. So in the file I posted above, if you want the relative BA/2B/3B/HR factors for parks, which in theory will also produce the same scoring environment you can use that one as well. You can set every park to 1.05 for HR and they will all actually play like 1.000 if you do this. You can invent your own ratings if you want. Where are you obtaining the data? How is it being formatted for use in OOTP? Any impact that the wind and temperature had on the ballparks historically should have been accounted for in the stats and hence in the factors. Now, in OOTP here is also a weather file. I use a custom file because I believe that the weather file actually functions as another factor in the game. As a rule of thumb, if you were to set BA/2B/3B/HR all to 1.05, or 5% above average, expect your scoring to go up at least double that, so more than 10%. So if you use edited factors you may not recreate the proper scoring environment if the factors are not correct for the game. I think in general people make too much out of the park factors. You have to play half of your games on the road anyway, you can't have a home version and an away version of your team. Let's consider team A whose park yields 20% more HR and let's consider a player on team B who hits 40 HR playing in a completely average ballpark who then gets moved to team A. Well, in league with 15 teams, those HR factors should add up to 15.000. Team A has a park with 1.200 for HR, that means the other 14 parks must equal the other 13.80. Ok, 13.80/14 = .985. This player now plays half of his games in 1.200 and half in .985. OK, so 1.200+.985 = 2.185 and 2.185/2 = 1.092. So how many HRs does this guy now expect to have this season? 1.092*40 = 43.68. So about 3 more HRs for the season. If he is a guy who hits 9 HRs per year, he might be expected to hit 10 with team A.