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# Sabermetrics / Baseball action forecasting

Member Posts: 34 Contributor II
edited November 2018 in Help
Dear everyone!

In my freetime, I'm playing for a baseball team.

Usually, there is one person showing the batter how to play the ball using a series of signs.
An example: Touches his nose, swipes over his shirt and touches his legs.

One of these signs tells the batter which action to take (hit the ball, do a bunt or whatever) and the rest of the signs is just to make it harder for the other team to know what will happen.
The "real" sign may be the first, second or any other sign in the row of signs he shows.
Sometimes, he also only shows any signs that haven't been communicated before which usually means, that the batter should play how he wants to.

To my question:
Is there a way to create an algorithm that analyses the signs that were shown, compares them to the actions that were taken by the players before and then shows a probability which action will be taken by the batter?
Of course, the user of the algorithm would have to input a series of data before like:
Signs: touch hand, touch nose, clap
Action: Bunt

• RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 578 Unicorn
Naive Bayes would be a simple model for this because it treats each attribute independently & so you can see how the distributions correspond to each type of pitch.
But if the coach is canny then he might be using a combination of signals.
Let's that if he has the actions:
- action 1 (touch nose)
- action 2 (piqué turn)
- action 3 (cough 3 times)
- action 4 (plie)
- action 5 (pirouette)

Actions 1,2,3,4 & 5 might mean nothing separately, but 2+4+5 together might mean that the batter should 'go long' (is that a baseball term? I've never played).
Anyway, in this case Naive Bayes might still be helpful, but association rules might be better as it would help you to visualise which combination of actions when used together signal a certain action.
• Member Posts: 34 Contributor II