Source: nflfastr. They have rich play-by-play data for every NFL game of the last ~25 years. I was able to get a time series of win probabiltiy for every Super Bowl since the 2000 season.

Tools: R. nflfastr to get the data, data.table to clean it and develop excitement scoring metrics, and ggplot2 to visualize.My collaborator built interactive visualizations for this project in Flourish. Those visualizations are linked lower in this comment.

Explanation: I've seen win probabilty graphs used as a shorthand for the excitement of a game. I wanted to develop a metric which takes in a win probability time series and outputs an "excitement score."Ultimately, I decided on three different factors that should contribute to the excitement score...

1. How close is the average win probability to zero? This is intended to capture how surprising was the eventual outcome.
2. What is the average absolute distance between the win probability and 50%? This is intended to capture how closely contested the game was.
3. What is the root mean square of all the changes in win probability from one play to the next? This is intended to capture how "back and forth" the game was.

I took each of these scores, scaled (but did not center) them, and then used their euclidean norm as the composite score.

Visit this webpage for more information on this topic!

The plots above are ordered by the composite score descending from left to right, top to bottom.It seems to work pretty well! Especially at the lower end of the scale--those are all pretty clearly games that were lopsided and foregone conclusions early on.

I've gotten the feedback that Super Bowl LIII between the Patriots and Rams is evidence of a "bug" in the metric. That game was very tight--which accounts for its high score (in the metric sense), but it was tight because it was low scoring (in the FOOTBALL sense!) with neither team performing very well.

Stuff I might tinker with to make it better:

1. Assign different weights to the three constituent metrics
2. Weight the constituent metrics differently at different points in the game.
3. Factor in how much scoring is happening

Great share! Now can you assign a quantitative way to rank Super Bowl snacks?

Having Super Bowl 53 ranked that high as an exciting game is criminal, you need to go back to formula.

I know, it's a real problem!

I think simply factoring in the sum of scores (or maybe the sum of scores averaged over every play of the game) would go a long way towards pushing that one down the rankings.

It's also been pointed out to me that SB 38 is remembered as especially exciting, but it's right in the middle of my rankings.

¯\_(ツ)_/¯ I'm sad the first attempt has such glaringly "wrong" results, but this is why we get feedback and iterate!

Thank you!

Great idea. Maybe a composite score of saltiness, cheesiness, and ranch-iness.

Until you can factor in the "Eye Test" you're gonna have a hard time. It's why scouts still have jobs.

You could consider increasing the weight of Q4/OT. Pats panthers was especially exciting toward the end, and great games in general have great fourth quarters.

Thank you! I think this is a really good suggestion.

Everyone talking about games which should be higher/lower but all I'm seeing is "New England being in the Superbowl has a dramatic increase in the excitement of the game"

Have you tried this data with Chat GPT to see what answer you would get?

Chatgpt might be able to tell an exciting game but that’s bc of volume of articles in it’s training data saying “so exciting, what a game” after the fact and only up until 2021. Perhaps it developed an understanding somewhere in those billions of parameters, or perhaps it is only regurgitated human opinion.

Scouts have jobs bc sports orgs don’t understand stats

Of course the team that reached the lowest win probability is a certain New England team. One would think that if it were that low then they should have lost...

Leave it alone

I only started watching NFL this year (in week 11 of the 2022 season). It took me a while to decode the labels, which require quite a bit of specialist knowledge of the NFL and there is no key. Marks off for that.

Once I tuned in though, I did find this interesting. How were the probabilities worked out though? Where is the background on this?

You’re right the labels are not very accessible to people without quite a bit of NFL domain knowledge. I had a hard time getting relevant info into them without them getting too long or taking too much real estate in the overall viz…but of course there should be a key. Thanks for the feedback!

nflfastr provides the win probabilities. the calculate win probability function docs have more info!

Thank you again for the comment!

I asked chat gpt for brief descriptions of various super bowls to help me remember how they played out so i could assess the ranking as I was building it. I also experimented with asking it broadly and non quantitatively “was this super bowl exciting?” “was that one?” to see if it was directionally agreeing with my scoring.

I did not go so far as to try and involve chat gpt directly in computing the score. How might that work? Simply ask it “please give a 1-10 score to the excitement level of recent super bowls”?

Wow dude. That's fascinating.

I guess... I've never really thought about it until now. Your post gave the idea. I wonder if it can give you predictions based on information?

Is it deemed more exciting if the winner’s win probability is low throughout the game?

If a team is heavily favored, even if the score is tied, their win probability is higher, meaning a back and forth game where the favorite has a narrow lead can skew this graph.

I think the Rams were favored in that dreadful SB LIII, and the biggest lead of the game was 10, meaning win probability stayed pretty level.

Cool data points. Like all data, it can be misleading

“winners win probability is low throughout the game” is one of the three factors influencing the composite score. (if it were the sole factor, the patriots falcons bowl would be the most exciting game by far)

The other two factors are “win probability closeness to 0.5” and “win probability changes”

You’re right, that awful rams super bowl won the “closeness to 0.5” category, which is why it’s ranked top 5

And yeah, it’s a fun cool project but not perfect. The best way to assess super bowl excitement is still to sit down and watch!

538 did this exact study last week with a better methodology and presented better.

Well at least you let me down gently!