Wiffleball -- Theoretical & Experimental Probability - 21st Century Math Project

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Theoretical and Experimental Probability gets a 21st Century Math Project facelift in this interactive game. Tired of flipping coins and spinning spinners? Students play the role of team managers and with only player statistics they must build a team capable of winning the championship.

Students will plays a series of Wiffleball games where they will keep track of statistics using a baseball/softball inspired scorebook and after the game crunch the number to see if their players performed up to their theoretical expectations. How does experimental probability match up? Students will find out together.

And your class can crown a champion if you choose to use the tourney bracket (included!)

In this 11 page document you will be given a mapping to the Content Standards, an outline for how to implement the project, a rules handout, the Scorebook, Player Cards, handouts for before and after the games and an 8-team championship bracket.

-- In "Pre-Game", students will choose a team of players and complete an assignment asking them to make predictions such as what the theoretical batting average of their team will be.

-- In "Wiffleball", students will faceoff against an opponent, draft a team, keep track of their statistics and see if they can win the championship!

-- In "Inside the Numbers", students will complete an analysis of theoretical vs. actual probability. Students will evaluate which players performed above or below expectations from the perspective of a team manager.

Dice not included.

Since this projects is driven by the student's individual choices and results, an answer key is not applicable.

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Total Pages
17 pages
Answer Key
Teaching Duration
3 days
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to see state-specific standards (only available in the US).
Understand that, just as with simple events, the probability of a compound event is the fraction of outcomes in the sample space for which the compound event occurs.
Develop a probability model (which may not be uniform) by observing frequencies in data generated from a chance process. For example, find the approximate probability that a spinning penny will land heads up or that a tossed paper cup will land open-end down. Do the outcomes for the spinning penny appear to be equally likely based on the observed frequencies?
Develop a probability model and use it to find probabilities of events. Compare probabilities from a model to observed frequencies; if the agreement is not good, explain possible sources of the discrepancy.
Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.
Understand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring. Larger numbers indicate greater likelihood. A probability near 0 indicates an unlikely event, a probability around 1/2 indicates an event that is neither unlikely nor likely, and a probability near 1 indicates a likely event.


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