HomeWorldData[OC] Visualizing Shohei Ohtani’s Chase for a 50-50 Season: Simulation of Home Runs and Stolen Bases with Probabilities for 30-30, 40-40, and 50-50 Clubs
[OC] Visualizing Shohei Ohtani’s Chase for a 50-50 Season: Simulation of Home Runs and Stolen Bases with Probabilities for 30-30, 40-40, and 50-50 Clubs
August 29, 2024
[OC] Visualizing Shohei Ohtani’s Chase for a 50-50 Season: Simulation of Home Runs and Stolen Bases with Probabilities for 30-30, 40-40, and 50-50 Clubs
Posted by wolverinelord
3 comments
I created this visualization to track Shohei Ohtani’s pursuit of a 50-50 season—achieving 50 home runs and 50 stolen bases in a single MLB season. Using data from ESPN, I simulated the remainder of his season to estimate the probabilities of reaching the “clubs”: 30-30, 40-40, and 50-50.
The 50-50 club refers to a player hitting 50 home runs and stealing 50 bases in a single season, which has never been accomplished in MLB history. It requires a rare combination of power and speed: the 40-40 club (40 HR, 40 SB) had been joined by only five players before this year.
I built a simulation model to project Ohtani’s performance over the remaining games of the season. The model uses his current stats as a baseline and generates a range of possible outcomes based on typical variability in player performance. To stabilize the projection at the beginning of the season, I used a Bayesian prior based on his historical stats. As the season goes on, the prior is given less weight so that the current season’s rates start to take over.
3 comments
I created this visualization to track Shohei Ohtani’s pursuit of a 50-50 season—achieving 50 home runs and 50 stolen bases in a single MLB season. Using data from ESPN, I simulated the remainder of his season to estimate the probabilities of reaching the “clubs”: 30-30, 40-40, and 50-50.
The 50-50 club refers to a player hitting 50 home runs and stealing 50 bases in a single season, which has never been accomplished in MLB history. It requires a rare combination of power and speed: the 40-40 club (40 HR, 40 SB) had been joined by only five players before this year.
I built a simulation model to project Ohtani’s performance over the remaining games of the season. The model uses his current stats as a baseline and generates a range of possible outcomes based on typical variability in player performance. To stabilize the projection at the beginning of the season, I used a Bayesian prior based on his historical stats. As the season goes on, the prior is given less weight so that the current season’s rates start to take over.
Data Source: [ESPN](https://www.espn.com/mlb/player/_/id/39832/shohei-ohtani)
Tools Used: Pandas, NumPy, Matplotlib
Beautiful data, congrats.
Still think there is a good possibility he should get a Pete Rose type gamblimg lifetime ban and the clubhouse guy was just a fall guy.