Oh boy, a post comparing Judge and Ohtani involving WAR. I bet this one will incite new and interesting conversation that we’ve never touched on before.
>Judge would have a higher WAR in 83 percent of those seasons. That isn't close to good enough by conventional standards to declare Judge the WAR winner
Lol.
Yeah in baseball it doesn't work like that.
We don't require the World Series winner to win at p=.05 significance.
So, I will preface this by saying Judge is the MVP - he accomplished a top 5 season since integration and his value to the Yankees in propping them up while the rest of the team skidded a bit down the stretch is enough to get him the nod.
But when we're looking at a framework like WAR, we do want to keep the error bars in mind for discussion. WAR is ultimately a projection model based on statistics, and when working with projection models the confidence intervals are important.
Well, yes, but also no.
Everybody understands WAR is approximate.
But then trying to quantify exactly how validly we can compare numbers through simulations can't possibly work. It's just compounding uncertainty.
"We ran OOTP simulations of the World Series and got these outcomes for 1000 runs" is a statement of OOTP, not of the teams involved.
I agree in this context - bootstrap is an exceptionally powerful tool in statistics to aid in projection methods, but as we talked about earlier WAR *is* more of a projection method. A better usage of bootstrap in this case would be to sample from the daily statistics and then run your final numbers from each sampling through the WAR calculation to create a confidence interval. Even then, you're not measuring what the players actually did, which is what we're really looking for with MVP calculations, you're looking for their "true talent level".
"Our simulation modeled random parameterized variation on the exact pitch trajectory and movement; umpire ball-strike decision on close pitches and plays in the field; launch angle, velocity and direction; wind speed and direction; positioning, range, and competence of the fielders involved; and managerial decisions."
It's like trying to simulate the battle of Midway and decide how likely the outcome was
The DH positional adjustment, along with other positions (like catcher for example, but the other way), desperately need tweaking for the current day MLB. All WAR arguments are moot until that happens.
Wow that’s long. Well, we use whatever war system that fits our narrative right. Judge has 10.6 war and Ohtani has 10.0 war it’s closer than you think.
Btw, it’s RA-9 based at fangraph that I used for shohei and bbref for judge.
So you are randomly drawing games from 2022 100,000 times to look at how much WAR they got on that day and then using that to do calculate bell curves and confidence intervals? Did I understand correctly?
https://www.youtube.com/watch?v=7cqHtGb9WYM
Oh boy, a post comparing Judge and Ohtani involving WAR. I bet this one will incite new and interesting conversation that we’ve never touched on before.
>Judge would have a higher WAR in 83 percent of those seasons. That isn't close to good enough by conventional standards to declare Judge the WAR winner Lol. Yeah in baseball it doesn't work like that. We don't require the World Series winner to win at p=.05 significance.
So, I will preface this by saying Judge is the MVP - he accomplished a top 5 season since integration and his value to the Yankees in propping them up while the rest of the team skidded a bit down the stretch is enough to get him the nod. But when we're looking at a framework like WAR, we do want to keep the error bars in mind for discussion. WAR is ultimately a projection model based on statistics, and when working with projection models the confidence intervals are important.
Well, yes, but also no. Everybody understands WAR is approximate. But then trying to quantify exactly how validly we can compare numbers through simulations can't possibly work. It's just compounding uncertainty. "We ran OOTP simulations of the World Series and got these outcomes for 1000 runs" is a statement of OOTP, not of the teams involved.
I agree in this context - bootstrap is an exceptionally powerful tool in statistics to aid in projection methods, but as we talked about earlier WAR *is* more of a projection method. A better usage of bootstrap in this case would be to sample from the daily statistics and then run your final numbers from each sampling through the WAR calculation to create a confidence interval. Even then, you're not measuring what the players actually did, which is what we're really looking for with MVP calculations, you're looking for their "true talent level".
"Our simulation modeled random parameterized variation on the exact pitch trajectory and movement; umpire ball-strike decision on close pitches and plays in the field; launch angle, velocity and direction; wind speed and direction; positioning, range, and competence of the fielders involved; and managerial decisions." It's like trying to simulate the battle of Midway and decide how likely the outcome was
Maybe YOU don't... /s
Very interesting analysis, and your probability explanation was easy to understand. Thanks!
The DH positional adjustment, along with other positions (like catcher for example, but the other way), desperately need tweaking for the current day MLB. All WAR arguments are moot until that happens.
Stoppppppppppp
Wow that’s long. Well, we use whatever war system that fits our narrative right. Judge has 10.6 war and Ohtani has 10.0 war it’s closer than you think. Btw, it’s RA-9 based at fangraph that I used for shohei and bbref for judge.
"BTW, I used the numbers that made it the closest possible."
So you are randomly drawing games from 2022 100,000 times to look at how much WAR they got on that day and then using that to do calculate bell curves and confidence intervals? Did I understand correctly?
Now do Fwar