Against the Data: Newman Was Wrong, But Does His Analysis of NFL Data with Celonis Reveal Why the Be
Updated: Mar 30
We were wrong. Well, Newman was.
He thought the Bengals were going to pull out a win, partly based on running NFL data through Celonis’ process mining platform (read the blog here, you can scroll down to watch the original video below too).
However, as they say in the NFL, that’s why they play the games. Analyzing data is a great way to identify trends and make predictions. When NFL coaches use data, they can increase their chances of putting their players in the best position to succeed.
Newman predicted that the Bengals would win by putting their faith in Joe Burrow passing the ball. The Bengals had an opportunity to win or tie late in the 4th quarter down by 3. On their final series of the game the Bengals went with the following series of plays: PASS, PASS, RUN, PASS. The third play (RUN) was on 3rd and 1 yard to go and was stuffed by Aaron Donald.
In Celonis, we can see that when the Bengals pass on the first two downs of an offensive series they never score when they rush on the third down. Instead, they score 0.84 points when they pass again on third down.
Unfortunately for the Bengals' chances of winning the Super Bowl, this dynamic played out according to the data. The image below shows the results of three consecutive passes and points versus two passes then a run.
There is a good argument to make that a quick run could have taken the Rams’ defense by surprise and gotten an easy first down. After all, going against tendencies and process can be a smart move on the football field to confuse the opponent. However, trusting in Burrow is probably the right call (even without the benefit of hindsight): the Bengals offense was rolling, there was limited time, and a seasons’-worth of data indicated that trusting Burrow and passing again on 3rd down would’ve significantly increased their odds to score the winning touchdown (or at least getting into position for a game-tying field goal attempt).
We see the case here where Celonis accurately predicted the outcome of the play. Unfortunately for Newman, the play calling of the Bengals led them to a loss in the Super Bowl -- maybe because they didn’t have Celonis.
Therefore, if the Bengals had left the game in Burrow’s hands, the data says Newman had a good chance of his pre-game prediction being true.
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