Which Metrics Predict Year 2 Breakouts? – The Wrong Read, No. 74
Image Credit: Larry Radloff/Icon Sportswire. Pictured: Chris Olave.

Earlier this week I explored George Pickens’ chances for a Year 2 breakout. While he’s got a lot going in his favor, there are some red flags. But it does raise the question of which metrics actually matter for predicting Year 2 breakouts. Which red flags should we actually be concerned about? Which metrics are so important that they drown out any red flags?

How to Find Which Metrics Predict Year 2 Breakouts

To answer these questions I used the RotoViz Screener to get rookie and Year 2 data for every wide receiver in the NFL since 2000. It’s possible to use the Screener directly to run a linear regression, but it’s hard to use that output to rank metrics in terms of importance. Instead, I split the dataset according to whether a player scored 200 points in Year 2. The goal is to see what differences in Year 1 metrics arise when we compare those players who broke out in Year 2 against those who did not. (We’re ignoring players who scored 200 points in Year 1 — those players count as rookie breakouts.)

Scaling the Data

In order to avoid problems of scale, I standardized each metric using z-scores. The idea there is to transform each metric from its native unit (yards, touchdowns, percentage points, etc.) into a unit that can be compared across metrics. Z-scores do this by telling us how many standard deviations above or below a given metric’s mean each observation falls.

How to Understand Z-Scores

For example, the average height of every player in the dataset is about 72.6 inches. Pickens’ height of 75 inches is exactly one standard deviation above the average, so his height gets a z-score of 1.0. Drake London’s height of 76 inches is 1.5 standard deviations above the average, so he gets a score of 1.5 for height. On the other hand, Wan’Dale Robinson’s height of 68 inches is two full standard deviations below the mean, so he gets a score of -2.0.

Once we have these z-scores, we can average and compare them based on whether a player broke out in Year 2 or not. The chart below shows the average z-score in each metric for breakouts and non-breakouts.

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Blair Andrews

Managing Editor, Author of The Wrong Read, Occasional Fantasy Football League Winner. All opinions are someone else's.

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