topic Re: Challenge #18: Predicting Baseball Wins in Weekly Challenges

Challenge #18: Predicting Baseball Wins

GeneR — Wed, 23 Aug 2023 19:49:38 GMT

This week let’s have some fun and look at predicting baseball wins. For those looking for the solution to last week’s (challenge #17) the link is HERE.

The use case: The Baseball season has completed and it's time to project next year's win totals.

The objective: Determine the top 10 variables that correlate to wins (excluding [Win_Pct] and [Games] from the correlation). Leverage those top 10 variables to predict the # of wins the team will have in next year’s season.

Isolate the teams to only use Boston - BOS, Los Angles of Anaheim - LAA, Chicago Cub - CHC, San Francisco Giants - SFG, Colorado Rockies - COL and Texas Rangers - TEX.

Create what the final standing will be and how many games out of first place each team is assuming each team plays 162 games.

Good luck, I hope you are having fun with these exercises and expanding your knowledge of Alteryx. Thanks to all that have provided feedback

Re: Weekly Exercise #18: Predictive – Predicting Baseball Wins (Intermediate)

TaraM — Mon, 28 Mar 2016 17:53:57 GMT

A solution has been posted to this article.

Re: Weekly Exercise #18: Predictive – Predicting Baseball Wins (Intermediate)

alex — Mon, 28 Mar 2016 22:26:12 GMT

I pretty much had the same solution except I used a multi-row tool instead of Summarize/Append to calculate games behind.

Re: Challenge #18: Predicting Baseball Wins

SeanAdams — Wed, 29 Mar 2017 04:07:03 GMT

Pretty much the same solution as @GeneR / @TaraM and @alex. @alex's method of using a join to filter instead of a formula is much more scalable on large data.

Request to @TaraM - some of the terminology in this one caught me - for example "how many games out of first place", which is probably a natural way of describing the question for folks who grew up with baseball.

Would you mind amending the challenge text a little to say something like "what is the difference in projected win-count between the overall winner and each other team" or something similar?

Good challenge - useful to have an excuse to use the predictive tools!

Re: Challenge #18: Predicting Baseball Wins

estherb47 — Thu, 27 Apr 2017 03:32:46 GMT

Fun!!

Re: Challenge #18: Predicting Baseball Wins

Laurap1228 — Wed, 10 May 2017 10:38:55 GMT

There are a few different ways to determine the top 10 metrics for predicting wins. I used the Association Analysis tool.

Re: Challenge #18: Predicting Baseball Wins

LordNeilLord — Wed, 10 May 2017 11:58:25 GMT

First time using the "association analysis"....worked well :)

Re: Challenge #18: Predicting Baseball Wins

NicoleJohnson — Wed, 17 May 2017 04:44:10 GMT

My solution! I saved this one till last because I didn't read the question all the way (or at all) and for some reason thought it had something to do with drafting a Fantasy Baseball team, and I was hoping to modify it later to help get a leg up on Fantasy Football drafting later this summer... turns out, that was not at all what the challenge was about. Oops. However, dearest Alteryx Challenge-Generating Team, I do believe a Fantasy Football Drafting Challenge would be a lovely idea for an early summer brain teaser... just sayin'... ;)

Anyway. I originally had more or less the same workflow as others, but decided to take a slightly different detour because I don't like having to go in and manually select things (like variables) in my workflows. So I figured out a (rather rambling) path that more or less allowed me to select all variables (except Tm & Wins) in the Linear Regression tool by narrowing it down to just the 10 top variables in the data being used in the tool first (rather than solving for the 10 variables first and then manually picking them from the list of all variables).

(It's the little things in life that bring me joy...)

And on that note... I believe I have officially finished all 70 of the current Weekly Challenges to date!! Race you to home plate (i.e. 75 Challenges), @SeanAdams & @estherb47 ! :)

Re: Challenge #18: Predicting Baseball Wins

SeanAdams — Wed, 17 May 2017 08:18:15 GMT

Game on @NicoleJohnson . Will be interesting to see the action replays of the photo finish for the 75th one.

Any other takers in the community to make this a 4 or 5 way sprint to the finish line?

Re: Challenge #18: Predicting Baseball Wins

estherb47 — Wed, 17 May 2017 14:01:16 GMT

@SeanAdams, @NicoleJohnson, don't know about racing to the finish (I can't always get to the challenges when they're posted) but I will 100% cross the finish line with you!!!

Are you going to Inspire this year? I'd love to meet up in person if you are. I will be there, and cannot wait. Last year was, well, quite inspiring :)

Re: Challenge #18: Predicting Baseball Wins

NicoleJohnson — Wed, 17 May 2017 15:04:26 GMT

I will be at Inspire! Let's definitely meet up!! I'll send you a message :)

Re: Challenge #18: Predicting Baseball Wins

SeanAdams — Wed, 17 May 2017 17:15:21 GMT

Sadly I'm missing Vegas this year but will be in London for Inspire EU. Already making sneaky squirrel plans tho' for Vegas Inspire 2018

Re: Challenge #18: Predicting Baseball Wins

DE0413 — Thu, 25 May 2017 15:41:30 GMT

This was a difficult, but fun challenge. Solution is similar to others, but probably could be condensed.

Re: Challenge #18: Predicting Baseball Wins

LordNeilLord — Thu, 01 Jun 2017 11:53:14 GMT

Workflow

Re: Challenge #18: Predicting Baseball Wins

ADerbak — Thu, 31 Aug 2017 15:26:57 GMT

I liked this one a lot! Solution attached.

Re: Challenge #18: Predicting Baseball Wins

jamielaird — Tue, 05 Sep 2017 16:36:45 GMT

Here's my solution. I spotted an anomaly in the dataset in that CHC has 161 games historically but we're asked to assume 162 games for the prediction. I corrected it using a formula tool but it actually makes no difference at all to the scores (even when you look to multiple decimal places). Interesting!

**EDIT: Thinking about this, Games isn't included in the model so why would it make a difference anyway? Eh Jamie? What were you thinking? Anyway, I'll do the right thing and leave this here as a memorial to that one time I got it wrong.**

Re: Challenge #18: Predicting Baseball Wins

patrick_digan — Mon, 18 Sep 2017 16:08:28 GMT

Re: Challenge #18: Predicting Baseball Wins

MaxGiegerich — Tue, 26 Sep 2017 16:45:46 GMT

Tough but a useful push into learning some of the core tools of Alteryx. Score is the one to remember by the looks of things!

Re: Challenge #18: Predicting Baseball Wins

samjohnson — Mon, 16 Oct 2017 19:56:01 GMT

This was a challenge that I burned too many brain cells over, for sure.

UPDATE: I challenged Alteryx and myself to find a better solution than a multivariate linear regression model, so I've included tests of five separate supervised algorithms and used the model comparison tool to select the best model. The winner was the Neural Net using the three principal components. There are advantages to using PCA results with neural nets, as can be found in an answer here: https://stats.stackexchange.com/questions/67986/does-neural-networks-based-classification-need-a-dimension-reduction. The neural net model has a mean absolute error of LT 2, which is both fantastic and raises some skepticism that it's overfitting and even memorizing the data, and the root mean squared error is still 2 1/2x less than the Random Forest model to almost 4x less than any other model.

The team statistics are normally distributed, a plus. But, there are a lot of highly correlated attributes as baseball statistics are a mix of raw compilations and computed statistics; for example, OPS is On-base Percentage (OBP) + Slugging Percentage (Slug). Highly correlated variables create collinearity, which makes regression models freak out by making some variables more important or less important than they should be. There are a number of ways to handle highly correlated predictor variables, but to follow this challenge's directions I highlighted the top 10 variables (which as a data scientist I would not do), and then I noted that those variables created collinearity; for example, Runs and Runs per Game are really the same and highly correlated with one another. I handled this problem by creating principal components, and found that 2 PCs accounted for 93+% of the variance within the data, an incredibly high coverage.

My wins predictions are somewhat different from most other solutions, but I believe this is true because my regression model's R-squared of 0.3726 and adjusted R-squared of 0.3002 indicate the reduced collinearity created by the tightly correlated top 10 variables, where other solutions indicated results of an R-squared of 0.5571 and adjusted R-squared of 0.3241, indicative of an internal collinearity issue due to the adjustment. The model is confused as to which variables are the real indicators of predicted wins.

Re: Challenge #18: Predicting Baseball Wins

JacobR77 — Tue, 17 Oct 2017 12:06:43 GMT

This was new for me! Had a look at the solution to help me along the way.