## Monday, October 16, 2023

### (R) Utilizing Crowd Prediction Methodologies to Draft the Optimal Fantasy Football Team (II)

What would an application be without proof? A notion?

To prove that the ADP drafting strategy is superior to other ranking methodologies, I performed the following analysis.

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Data Source(s):

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Offense

Kickers

DST

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The Analysis (n = 300)

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ADP <- c(1.4, 2.4, 2.7, 4.2, 4.9, 5.8, 7, 7.2, 8.8, 10, 10.6, 12.4, 13.1, 14, 15.3, 16, 16.6, 17.9, 18.5, 19.1, 19.6, 19.8, 21.8, 22.7, 24.7, 25.9, 26.1, 27.3, 27.9, 29.3, 30.5, 31.6, 32.4, 33.2, 33.2, 34.1, 36.1, 38, 39.1, 39.3, 39.4, 39.7, 42.3, 43.2, 43.5, 44.2, 45.1, 46.5, 46.8, 47.3, 48.4, 50.2, 50.9, 51.5, 52.2, 53.9, 54.1, 56, 56.7, 57.9, 59.4, 61.2, 61.5, 62.1, 62.9, 63, 63.8, 63.9, 68, 68.1, 69.2, 69.9, 70.1, 71, 71.4, 71.7, 72, 72.9, 75.3, 75.4, 77.1, 77.9, 82.3, 83.8, 84.4, 84.8, 85, 86.7, 87.9, 89.5, 90.2, 90.2, 90.7, 91.3, 91.7, 91.8, 94.4, 95.9, 96.2, 98.5, 98.7, 99.1, 101.9, 102.6, 102.7, 103.6, 103.7, 105.4, 106.5, 106.7, 107.8, 108.6, 109.7, 109.9, 110.6, 111.6, 113.1, 113.6, 113.9, 114.9, 117.3, 117.7, 118.5, 119, 119.6, 120.5, 121, 121.2, 121.3, 122.7, 124.2, 125, 128.9, 129.8, 130.2, 130.2, 130.3, 130.8, 131.2, 132, 135.1, 135.9, 136.1, 136.3, 137.9, 138.9, 140.1, 140.9, 141.8, 142.7, 143.8, 144.8, 145.3, 145.4, 145.7, 147, 147, 148.1, 148.4, 148.9, 149, 149.4, 151.3, 151.4, 152.1, 152.5, 152.6, 153.8, 154.9, 155.6, 155.7, 156.7, 156.8, 156.8, 158, 158.5, 158.8, 158.9, 159.4, 160.1, 160.5, 160.7, 161, 161.5, 162, 163, 163.5, 164, 164.6, 164.6, 165, 165.2, 165.6, 166, 167, 168, 169, 169.5, 170, 171, 172, 173, 174, 175, 176, 177, 178, 178, 179, 180, 180, 181, 182, 183, 184, 185, 186, 187, 187, 188, 189, 190, 190, 191, 192, 192, 193, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265)

PPR <- c(146.4, 356.36, 372.7, 302.76, 368.66, 201.4, 223.46, 237.8, 242.4, 239.5, 211.7, 335.5, 191.1, 316.6, 248.6, 284, 316.3, 301.6, 281.4, 395.52, 168.4, 42, 226.1, 347.2, 216.5, 190.5, 225.4, 185.8, 200.2, 164, 299.6, 75.6, 220.9, 281.26, 141.3, 205.1, 199.1, 229, 417.4, 177.7, 81.2, 176.5, 43.6, 200.5, 180.7, 115.1, 159.4, 259.2, 350.7, 328.3, 167.6, 84.8, 226.8, 236.08, 51.1, 84.9, 98.3, 378.04, 222.8, 145.6, 90.9, 204.2, 216.7, 200.52, 142.7, 52.2, 171.6, 180, 156, 101.5, 126.8, 248.8, 74.2, 166.4, 79, 177.9, 225.76, 267.6, 141.2, 185.3, 249.1, 239.2, 53.5, 246, 87.1, 198.6, 254.6, 271.66, 88.1, 115.6, 227.8, 151.7, 174.8, 105.7, 108.38, 148.7, 69.4, 202.5, 241.9, 88.6, 148.2, 12.46, 168.3, 219.08, 115.7, 178.6, 147.3, 57.3, 87.9, 159.4, 291.58, 55.8, 198.2, 112.7, 237.3, 126, 98.2, 73.5, 135.7, 165.9, 150, 43.4, 135, 105.4, 215.4, 225.9, 70.4, 230.92, 167.12, 139.1, 166.5, 87.7, 88.4, 102, 122.4, 7, 94.1, 114, 105.04, 155.28, 102.9, 160, 103.9, 101.6, 159, 102, 98, 161, 295.98, 295.62, 115, 87.9, 112, 133, 101, 103, 55.2, 43.92, 116, 97, 131, 180.3, 121, 130.6, 143, 142, 119.8, 215.7, 25.5, 125, 123.6, 99, 39, 110, 97.1, 130, 129, 97, 57, 18, 118, 154, 168, 100, 116.9, 20.1, 104, 98.2, 186, 97, 60.2, 110, 101, 52.2, 155.6, 93.8, 3.2, 110, 169.3, 97.6, 34.4, 161.1, 78.9, 25.8, 51.6, 112.3, 115, 176.3, 198.1, 106, 26.4, 82.7, 149.1, 117.8, 88.3, 81.7, 110.1, 83, 4.5, 50.1, 164.1, 73, 12, 57.68, 139, 61.6, 112, 77.4, 45.4, 46.5, 15.1, 35.5, 72.7, 75.2, 84.5, 110, 0.2, 142.1, 34, 12.2, 83, 82.6, 21.1, 77.2, 196.3, 11.4, 51.7, 8.4, 54.1, 161.24, 46.2, 8.6, 8.4, 13.8, 289, 37.8, 170.08, 128.4, 89.6, 112.8, 104.1, 284.32, 154.16, 59.3, 24.9, 114.5, 121.42, 158.5, 59.8, 98.92, 0, 115.1, 10.2, 181.52, 14.8, 4.2, 12.8, 18.8, 103.9, 196.56, 1.3, 11.8, 16.2, 26, 39.1, 43.1, 53.6, 103.8, 27.3, 303.88, 30.8, 64.5, 3.5, 73.88, 110.2, 64.7, 84.3, 30.5, 10.2, 70.3)

Which produces the output:

Pearson's product-moment correlation

t = -13.394, df = 298, p-value < 2.2e-16

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

-0.6791003 -0.5370390

sample estimates:

cor

-0.6130004

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Creating the Visual Output

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library("ggpubr")

ggscatter(my_data, x = "ADP", y = "PPR",

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

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

xlab = "ADP (2022)", ylab = "Player Score (PPR)")

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Conclusion

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There was a negative correlation between the two variables: ADP (n = 300), and Player Score (PPR). ADP (M = 134.81, SD = 73.31), Player Score (PPR) (M = 133.33, SD = 86.66), Conditions; t(298) = -13.394 , p < .01. Pearson Product-Moment Correlation Coefficient: r = -0.61.

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As the findings indicate, there is a significant negative correlation as it pertains to ADP and player performance (PPR - 2022 Season). In plain terms, this means that we should typically expect to see better fantasy performance from players with lower ADP rankings. I hope that everyone enjoyed this article and my adherence to APA reporting standards. <3

Until next time.