I lost my first mma fight at the end of the first round by tko. I knew I was better than the guy but he had 2 or 3 fights and I kept zoning out with everything going on. What sucks is the promotion puts it up and it hurts me knowing I did well until I didn’t. How do I get over it/any similar experiences?

I tried to reinvent the wheel going into week 2.

Things that didn't change since Week 1 (very little):

• Model is only capable of predicting fights where the method of victory is KO/TKO, SUB, or DEC (Draws, No Contests, Doctor's Stoppage, etc. are removed from the training data and disregarded altogether).

• The training data still consists of all fights since UFC 229.

Changes since Week 1 (so many that this list couldn't be comprehensive):

• Model now predicts percent likelihoods for all possible outcomes (though the method %'s are not scaled correctly).

• I fixed the issue of most likely methods not always being associated with most likely winners (it previously predicted most likely winners and methods separately and then merged them together).

• I am now primarily scraping the training data from instead of from (for a few reasons, but primarily for the one listed below).

• Added a ton of new features (such as fighter stances) with ufcstats.com which were otherwise unavailable using ufc.com.

Mistake and Correction:

I had hoped to post the predictions way before the event started, but found myself wanting to add as many new features as possible leading up to it. As a result, I found myself in a situation where I was running out of time.

^They were understandably not well-received at the time and the post was eventually locked for not being in the right thread.

Here is the mistake that led to those predictions:

It takes a long time to run this program (~3-4 minutes). Shortly before the event, in the interest of saving time, I modified the function/file used for hyperparameter tuning (which usually relies on the scikit-learn library) to use a faster, PyTorch-centric approach instead. In doing so, I inadvertently screwed up the hyperparameter tuning massively as well as removed several of the features I had implemented that were reliant on files outside of the original training dataset, such as ones containing method distributions by weight class and result distributions by stance matchup.

After correcting this mistake, I ran the program again and got the results shown below on the first go:

• The differences in predicted winners are that it now predicted Muslim Salikhov to win (Better?) and Mariya Agapova to win (Worse).

• The differences in predicted methods (and their percentages) are what got jacked up so much by me trying to use the aforementioned shortcut (custom PyTorch solution for hyperparameter tuning instead of letting the scikit-learn library do its thing).

 A few things I want to note moving forward:

• I omitted the Jasmine Jasudavicius vs. Fatima Kline fight because there was no data for Fatima Kline (UFC debut). To address this in the future, I'll be working on a method to automatically populate debuting fighters' stats with weight class averages for whichever weight class they're competing in.

• I'm planning on increasing the training data to include all fights going back to UFC Fight Night: Saffiedine vs Lim (January 4, 2014).

• I'm going to continue working on the method %'s and scaling, as well as anything else that comes to mind (there are still a lot of things I want to try). I am open to suggestions as well.

The biggest challenge I'm facing with this project is that I haven't figured out how to freeze time yet so that I could work on it forever. As always, let me know what you think or if you have any questions about the model, predictions, etc.

After watching one of those punch machine videos, I vaguely remembered that in like 2010 the UFC made a big deal that they were going to have like an NFL combine type event and test all their athletes. I tried to Google it, but struggled to find any updates. Did they ever actually do this, is there anywhere I could see the results, and does anyone know why they stopped?