Run Geno Run!

Eric Hardter

genosmith

To put it mildly, Jets rookie signal caller Geno Smith’s first year under center was a bit of a mixed bag. Though he definitively flashed moments of progress and promise (come from behind wins over Tampa Bay, Atlanta and New England, as well as a sterling final quarter of the season), there were more than enough lows to cancel out the highs. Long story short, be it due to his rawness, the scheme adjustment from college or a lack of quality pass catchers, Smith’s 2013 campaign left plenty to be desired.

With that said, at a minimum we still have 16 games worth of data to use in order to comprehend Smith’s freshman season. Given that, is it possible to decipher precisely which commonalities and trends made the now-sophomore quarterback tick? In an attempt to ascertain just that, consider the following table of Smith’s game-by-game production, summarized as a weekly heat map.

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As can be seen above, there were ten major quantifiers used to differentiate each week. Looking at Smith’s performance as a passer, I used passing attempts, yards, touchdowns, interceptions and points per pass attempt (PPA). For when he ran the ball, I looked at rushing attempts, yards, touchdowns and points per rush (PPR). This ultimately culminated in a weekly fantasy rank amongst his signal calling peers (shown in the far right column), based upon standard quarterback scoring.

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The average for each quantifier is also shown (third row from the bottom), as well as the benchmarks for when Smith performed either 25% better (green row, second from the bottom) or 25% worse (red row, bottom) than each statistical standard. As such, each cell in the table can be colored in one of three ways, resulting in the heat map. Green was used for an above average value (≥ +25%), white for an average value (± 25%) and red for a below average value (≤ 25%).

Let’s begin dissecting the table by starting with Smith’s standard weekly output, which was underwhelming to say the least. For the season Smith failed to average 30 passing attempts and 200 yards, and was also on the wrong side of the touchdown to interception ratio (0.75:1.31). This culminated in a relatively pathetic 0.329 points per passing attempt (PPA), a value that stood amongst the worst in the league.

Things were slightly rosier when Smith decided to tuck and run. On the year he turned 4.5 weekly rushing attempts into 22.9 yards, with an average of 0.38 touchdowns per game. This resulted in a robust efficiency of 0.818 points per rush (PPR), a figure which would rival that of the league’s top ball carriers.

Digging deeper, it was very nearly a tale of three seasons for Smith. Five of his first seven games resulted in either average (QB16-24 ) or above average (≥ QB15) performances, including three top ten finishes. Smith then slogged through a five game stretch with four below average performances (≤ QB25), including two finishes outside of the top-32 signal callers (yes, that math is correct). Finally, Smith finished the season with a flourish, with three above average performances in the last four games.

Continuing our descent into this quantitative breakdown, it’s unsurprising to see that the “hot” (green) and “cold” (red) zones of the heat map roughly align with the above paragraph. Games 1-7 yielded a smattering of green, red and white, which then shifted to nearly all red for games 8-12. In the last four games of 2013, Smith seemingly turned the corner as these quantitative qualifiers were very nearly all above average.

While this is somewhat useful as it relates to tracking Smith’s seasonal arc, it does little to aggregate his defining qualities, both good and bad. Since the ultimate purpose of this exercise is to illuminate exactly how his successes (or lack thereof) came to be, it makes sense to lump “like” games together in order to discern any possible trends. To that end, let’s start with Smith’s above average games.

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The subset of games above is shown in essentially the same format as the initial heat map, with identical cell coloration. The only difference is instead of using the numerical values for each quantifier, a sliding scale of -1 through +1 was used to denote whether each category was below average (-1, red), average (0, white) or above average (+1, green). The totals for each category were then summed up in the bottom row.

With that preamble in hand, the first thing of note for Geno’s above average games is the fact he scores the ball. As mentioned previously, Smith averaged less than one touchdown per game, so throwing (or rushing) for at least one score represents an aberrant performance. Within this subdivision of games, the total for both passing and rushing touchdowns stood at +4.

From there we begin to see a divergence between Smith’s passing and rushing statistics. His above average games had little to nothing to do with passing volume and yardage (totals of 0 and +1 respectively), whereas his rush attempts (+2) and output (+3) moved closer to the forefront. The totality of these factors led to increased efficiencies, both passing and running, with aggregate totals of +4 and +5 correspondingly, once again skewing in favor of his rushing output, even if only slightly.

Next, let’s examine Smith’s below average performances, displayed in an analogous manner to the above.

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Don’t get me wrong, there’s nothing pleasant about Smith’s passing statistics in his six below-average performances. He didn’t throw a single touchdown and was picked off multiple times in five of six games. The volume was there, but the passing yards were lacking in half of the contests – all told this led to a below average PPA value in all six games.

However, things were even worse rushing the ball. Given the six-game sample size and four quantifiers, Smith impressively made a mess of things all 24 times. The attempts were lacking, as were the yards, and he didn’t score once – not shockingly this resulted in a subpar efficiency for every game.

The summary of these two subsections can be shown in the table below. For both the above and below average games the “totals” (bottom rows) were aggregated for the passing and rushing quantifiers, and then compared to the theoretically possible sums. These were then broken down into percentages in order to discern which aspect of Smith’s game had the bigger impact on his divergent performances.

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If the point hasn’t been made yet, the table above should drive home the last nail in the statistical coffin. When Smith functioned above his mean level of production, nearly 60% of his rushing qualifiers were also above average. The passing qualifiers, while also largely positive, accumulated to calculate to a significantly lesser total of 36.7%.

Perhaps even more glaring are the totals from Smith’s below average performances. As mentioned previously, there was a direct relationship between his bad games and his bad rushing outputs, as his running qualifiers were subpar 100% of the time. His passing totals were also, to put it kindly, unsatisfactory – however, with a qualifier aggregate of -20 (out of a possible -30), these were less impactful than what Smith did on the ground.

Analogous to what the tables above show, it appears the Jets coaching staff also realized what Smith can produce when his rushing abilities are properly utilized. During the last four games of the season Smith’s average rushing line stood at 7.8/46.5/0.75, and these collective totals represented 43% of his yearly rushing attempts, 51% of his rushing yards and 50% of his rushing touchdowns. The touchdowns might come and go, but even factoring them out we can find that 29% of Smith’s scoring over the last four weeks came from his rush yardage – that type of usage is highly encouraging for the future.

Thus far in the 2014 preseason, we’ve seen more of the same. While it’s tough to take these glorified scrimmages at face value, the continuity from the end of 2013 should be taken as a positive sign.

There’s no doubt Smith still needs to improve as a passer – once again, he showed flashes at the end of the 2013 season, and perhaps more importantly the Jets improved his supporting cast with the signings of receiver Eric Decker and running back Chris Johnson, while also spending a second round pick on “move” tight end Jace Amaro. With that said, for fantasy purposes it’s clear his immediate value is more likely to come on the ground. Given this likelihood we can only hope the Jets coaching staff continues to act as the Jenny to Smith’s Forrest Gump, imploring him onward with one simple command – run Geno run!

Follow me on Twitter @EDH_27

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eric hardter