(R) Is Motion Prior to the Snap Correlated with Wins per Season?

The year of our Lord 2023, has been an anemic year as it pertains to NFL offensive prowess. There are numerous articles written as to why this may be the case. I personally believe that it may be due to an over reliance on the passing game. While this approach was optimal throughout prior years, recent defensive innovations devised to temper this phenomenon, have since severely limited its present effectiveness.

There are many methodologies which can be utilized to counteract the effectiveness of newly emergent algorithmic football defenses. The tried and true amongst such, is the application of motion prior to the snap.

In doing research for this article, I stumbled upon the following data:

So, in the spirit of upcoming Super Bowl LVIII (49ers vs Chiefs), let’s analyze this information in order to discern whether team wins are correlated with pre-snap motion.

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# Getting the Libraries in Order (for Graphical Output) #

library(ggpubr)

library(tseries)

# Motion Prior to Snap (through Week 6) #

# Data Collected by ESPNStatsInfo #

# Populate Data Frame #

Team <- c('Dolphins', 'Rams', '49ers', 'Lions', 'Packers', 'Chargers', 'Seahawks', 'Falcons', 'Ravens', 'Titans', 'Giants', 'Bears', 'Chiefs', 'Colts', 'Steelers', 'Texans', 'Jaguars', 'Jets', 'Broncos', 'Vikings', 'Washington', 'Bengals', 'Patriots', 'Buccaneers', 'Cardinals', 'Bills', 'Browns', 'Panthers', 'Saints', 'Raiders', 'Eagles', 'Cowboys')

Motion_Percentage <- c(80.2, 65.4, 77.5, 62.3, 58.3, 55.6, 48.9, 61.1, 49.5, 48, 44.3, 56.1, 68.3, 45.4, 45.4, 52.3, 44.9, 35.5, 38.1, 46.3, 53.2, 44.6, 51.1, 43.9, 39.9, 48.7, 47.3, 35.4, 28.5, 52.9, 21.7, 42.1)

Wins_2023 <- c(11, 10, 12, 12, 9, 5, 9, 7, 13, 6, 6, 7, 11, 9, 10, 10, 9, 7, 8, 7, 4, 9, 4, 9, 4, 11, 11, 2, 9, 8, 11, 12)

Motion_Report <- data.frame(Team, Wins_2023, Motion_Percentage)

# Derive Mean and Standard Deviation #

mean(Motion_Report$Motion_Percentage)

sd(Motion_Report$Motion_Percentage)

mean(Motion_Report$Wins_2023)

sd(Motion_Report$Wins_2023)

# Apply Correlation Methodology $

cor.test(Motion_Report$Motion_Percentage, Motion_Report$Wins_2023)

# Create Graphic Visualizations #

data <- data.frame(Motion_Percentage, Wins_2023)

ggscatter(data, x = "Motion_Percentage", y = "Wins_2023",

add = "reg.line", conf.int = TRUE,

cor.coef = TRUE, cor.method = "pearson",

xlab = "Rate of Motion (Through Week 6)", ylab = "Season Wins (2023)")

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Findings

There was a positive correlation between the two variables: Season Wins (2023) (n = 32) and Rate of Motion (Through Week 6) (n = 32). Season Wins (2023) (M = 8.5, SD = 2.747), Rate of Motion (Through Week 6) (M = 49.772, SD = 12.526), Conditions; t(30) = 1.4809, p = .15. Pearson Product-Moment Correlation Coefficient: r = .26.

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Conclusions

While the p-value findings (p = .15) can be viewed as non-significant at the alpha level of .05, we must take into account that there are certain experimental limitations which will innately confound our results. For one, wins per season is zero sum. Meaning, that a win for one team, is always loss for another. Also, as both motion and wins per season are discrete variables, there is a limited predefined range of differentiation which exists between each variable. This, combined with wins per team being non-independent, reduces the test result to a generalization.

Success of motion implementation is being assessed solely on wins, and the mechanism for generating such is being assessed by offensive motion alone. Our assessment does not account for strength of schedule, team defensive prowess, player fundamentals, etc.

However, that being said, with a p = .15, and a correlation coefficient value of r = .26, it likely is more fortuitous, all things being equal, to implement an offense which possesses pre-snap motion. There certainly are many other factors which can determine outcome which are not assessed within this model, but in all likelihood, they will not have a large impact upon the overall findings.