Recommended
Recommended
More Related Content
Similar to Data science in baseball
Similar to Data science in baseball (20)
Recently uploaded
Recently uploaded (20)
Data science in baseball
- 1. D ATA S C I E N C E I N B A S E B A L L P R O F . A B H A M A R A T H E
- 2. T E A M Harsh Tomar – 04 Harshvardhan Mulik – 05 Anushka Hedaoo – 06 Subodh Kadam – 22 Kartik Kakani -25
- 3. T H E R E ’ S M O R E T O W I N N I N G T H A N T R A I N I N G H A R D A N D S C O R I N G P O I N T S • From media contracts to licensing deals, ticketing and merchandising, and even how many beats per minute a player’s heart pumps on game day, data has essential information for strategic decision making.
- 4. W AY S T O U S E D ATA SCOUTING PLAYER FITNESS AND DEVELOPMENT GAME DAY STRATEGY PLAYER EVALUATION TICKETING MERCHANDISE
- 5. P L AY E R D E V E L O P M E N T • On-field wins often translates into a team’s financial success, so developing talent and adding new players adds obvious value to a club. Teams gather huge amounts of data on players to track multiple metrics such as training intensity, game performance, recovery time, and injuries. • Whole-team intelligence is important too. How are players working together? Are there patterns of play? Do various actions on the field make a difference in the final score? • Visualizing this data allows tech-averse coaches to interpret immediate needs at a glance.
- 6. G A M E D AY S T R AT E G Y • Data science and visualizations don’t just capture what happened in last week’s game; they can provide insight while the ball is still in play. • Dashboards help coaches predict outcomes with what-if scenarios by combining live data with historical data.
- 7. P L AY E R E VA L U AT I O N Visual dashboards are a powerful tool for accurate individual player evaluation. How is a player performing relative to last year? Last week? This data-driven evaluation clues coaches into who’s likely to play well and who may be heading for a slump. Coaches can adjust gameday lineups accordingly.
- 8. T I C K E T I N G • It is important to understand single-game demand for a variety of reasons, whether the team is thinking about running a promotion, or whether they are thinking about how to staff the stadium, it all goes to help produce the most amount of profit possible. • The model is trained on 3 years of game data to predict the number of tickets being sold. • By understanding the demand, they can schedule promotions around the most popular games- which will hopefully optimize the attendance and profit even more!
- 9. M E R C H A N D I S E • The home-team hat and jersey, beer, and bobble heads—what are people buying once they're inside? • With merchandising and retail data visualizations, retailers can adjust inventory on the fly when specific item revenue starts to dip. • Merchandise sales also reveal where a team’s fans live. Data can help identify new areas of the country, or the world, to concentrate sales efforts
- 10. S TAT- C A S T • In 2015, MLB (Major League Baseball), introduced StatCast in its games henceforth. • StatCast uses machine learning and computer vision to measure and evaluate plays during MLB games. We can see a player’s max speed, distance covered, first step and route efficiency. • StatCast has given both fans and teams insight that wasn’t available before. Players are now judged on hard data as opposed to subjective scouting feedback — this allows both players and teams to have a more accurate assessment of themselves and helps teams truly judge a player's value.
- 11. C O N C L U S I O N Data Science has become an integral part of Baseball over in years. Almost every decision may it be before, during or after the match is made using Data science and Visualization. It helps players, coaches and even the audience to understand the game better.