Blair Andrews searches through the advanced RB metrics to help you locate the breakout smash play of the 2024 season.
In previous installments of the RB breakout pieces we’ve looked both at how age and experience impact RB breakouts, and also at which metrics best predict future breakouts for non-rookies.
One question we haven’t investigated is why these breakouts tend to happen. Luckily we can approach this question in much the same way. While it’s true that many breakouts involve positive efficiency, that efficiency needs to be displayed when the player is on the field. In other words, we’re looking for players who can see an increase in opportunity. So what are the things that lead to opportunity? For the most part it’s metrics that add value (or perceived value) to an NFL team’s winning chances.
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The Metrics That Help NFL Teams Win . . . Will Help You Win
In the previous installment we noticed that broken tackles and missed tackles are two of the best predictors of coming breakouts, in part because those are plays on which an RB provides excess value for his team. But we have more direct measurements of value, such as expected points added and points earned, specialties of our data provider, Sports Info Solutions. These advanced metrics aim to measure the on-field impact of a player on his team’s winning chances. But how can they help fantasy players?
I’m investigating these metrics in the same way I did other advanced metrics. As a reminder, here’s the explanation I included in the earlier article.
To investigate what factors might signal a coming breakout, I pulled a bunch of RB metrics and transformed them into z-scores. The best way to think about a z-score is that it’s a version of a metric that is rescaled so that the average is 0 and the standard deviation is 1. Setting all metrics on a common scale like this allows us to make easier comparisons across metrics. And it allows us to compare each metric against a shared standard. A score of 1.5 always represents 1.5 standard deviations above the mean. Likewise a score of -1.5 always represents 1.5 standard deviations below the mean. It is for this reason that z-scores are sometimes referred to as standard scores.
After making this transformation we can compare breakouts against non-breakouts based on how they performed in these advanced metrics.
Separating RB Skill from Team Context
The chart below displays those results.
