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References
Aden-Buie, G., Schloerke, B., Allaire, J., & Rossell Hayes, A.
(2023). learnr: Interactive tutorials
for R. https://rstudio.github.io/learnr/
Adobe Express. (2020). 8 basic design principles to help you make
awesome graphics. https://www.adobe.com/express/learn/blog/8-basic-design-principles-to-help-you-create-better-graphics
Ægisdóttir, S., White, M. J., Spengler, P. M., Maugherman, A. S.,
Anderson, L. A., Cook, R. S., Nichols, C. N., Lampropoulos, G. K.,
Walker, B. S., Cohen, G., & Rush, J. D. (2006). The meta-analysis of
clinical judgment project: Fifty-six years of accumulated research on
clinical versus statistical prediction. The Counseling
Psychologist, 34(3), 341–382. https://doi.org/10.1177/0011000005285875
AIDSVu. (2022). Understanding the current HIV epidemic
in the United States. https://map.aidsvu.org/profiles/nation/usa/overview
Akinshin, A. (2023). Weighted quantile estimators. arXiv. https://doi.org/10.48550/arXiv.2304.07265
Altarejos, J., & Hayward, R. (2025). Likelihood ratio
nomogram. Centre for Health Evidence. https://jamaevidence.mhmedical.com/data/calculators/LR_nomogram.html
Andersen, D., Petersen, I. T., & Tungate, A. (2025). ffanalytics: Scrape data for fantasy
football. https://github.com/FantasyFootballAnalytics/ffanalytics
Atanasov, P., Witkowski, J., Ungar, L., Mellers, B., & Tetlock, P.
(2020). Small steps to accuracy: Incremental belief updaters are better
forecasters. Organizational Behavior and Human Decision
Processes, 160, 19–35. https://doi.org/10.1016/j.obhdp.2020.02.001
Ataneka, A., Kelcey, B., Dong, N., Bulus, M., & Bai, F. (2023).
PowerUp R Shiny app (v. 0.9)
manual. https://www.causalevaluation.org/uploads/7/3/3/6/73366257/r_shinnyapp_manual_0.9.pdf
Attali, D., & Baker, C. (2023). ggExtra: Add marginal histograms to ggplot2, and more ggplot2 enhancements. https://github.com/daattali/ggExtra
Austin, P. C., & Steyerberg, E. W. (2014). Graphical assessment of
internal and external calibration of logistic regression models by using
loess smoothers. Statistics in Medicine, 33(3),
517–535. https://doi.org/10.1002/sim.5941
Avugos, S., Köppen, J., Czienskowski, U., Raab, M., & Bar-Eli, M.
(2013). The “hot hand” reconsidered: A meta-analytic
approach. Psychology of Sport and Exercise, 14(1),
21–27. https://doi.org/10.1016/j.psychsport.2012.07.005
Awbrey, J. (2020). The future of NFL data
analytics. https://www.samford.edu/sports-analytics/fans/2020/The-Future-of-NFL-Data-Analytics
Baird, C., & Wagner, D. (2000). The relative validity of actuarial-
and consensus-based risk assessment systems. Children and Youth
Services Review, 22(11), 839–871. https://doi.org/10.1016/S0190-7409(00)00122-5
Baldwin, B. (2023). nfl4th: Functions to
calculate optimal fourth down decisions in the National Football
League. https://www.nfl4th.com/
Bales, J. (2012). 2012 contract year players and the myth of
increased production. https://www.4for4.com/2012/preseason/2012-contract-year-players-and-myth-increased-production
Bar-Eli, M., Avugos, S., & Raab, M. (2006). Twenty years of
“hot hand” research: Review and critique. Psychology of
Sport and Exercise, 7(6), 525–553. https://doi.org/10.1016/j.psychsport.2006.03.001
Barrett, M. (2024). ggdag: Analyze and
create elegant directed acyclic graphs. https://github.com/r-causal/ggdag
Bartoń, K. (2024). MuMIn: Multi-model inference.
https://CRAN.R-project.org/package=MuMIn
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting
linear mixed-effects models using lme4.
Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2025). lme4: Linear mixed-effects models using
Eigen and S4. https://github.com/lme4/lme4/
Bengtsson, H. (2024). progressr: An
inclusive, unifying API for progress updates. https://progressr.futureverse.org
Bengtsson, H. (2025). parallelly:
Enhancing the parallel package. https://parallelly.futureverse.org
Berry, S., & Wood, C. (2004). The cold-foot effect. CHANCE,
17(4), 47–51. https://doi.org/10.1080/09332480.2004.10554926
Bocskocsky, A., Ezekowitz, J., & Stein, C. (2014). The hot hand: A
new approach to an old “fallacy.” MIT Sloan Sports
Analytics Conference. https://www.sloansportsconference.com/research-papers/the-hot-hand-a-new-approach-to-an-old-fallacy
Bolger, F., & Önkal-Atay, D. (2004). The effects of feedback on
judgmental interval predictions. International Journal of
Forecasting, 20(1), 29–39. https://doi.org/10.1016/S0169-2070(03)00009-8
Bolker, B., & R Development Core Team. (2023). bbmle: Tools for general maximum likelihood
estimation. https://github.com/bbolker/bbmle
Bollen, K. A. (2002). Latent variables in psychology and the social
sciences. Annual Review of Psychology, 53(1), 605–634.
https://doi.org/10.1146/annurev.psych.53.100901.135239
Brown, M., Grasley, N., & Guido, M. (2025). Do sports bettors
need consumer protection? Evidence from a field experiment. https://mattbrownecon.github.io/assets/papers/jmp/sportsbetting.pdf
Bryan, J., Hester, J., Robinson, D., Wickham, H., Dervieux, C., &
Posit. (2025). Reprex do’s and don’ts. https://reprex.tidyverse.org/articles/reprex-dos-and-donts.html
Bulus, M. (2023). pwrss: Statistical
power and sample size calculation tools. https://CRAN.R-project.org/package=pwrss
Bulus, M., Dong, N., Kelcey, B., & Spybrook, J. (2021).
PowerUpR: Power analysis tools for multilevel
randomized experiments. https://doi.org/10.32614/CRAN.package.PowerUpR
Bürkner, P.-C. (2017). brms: An
R package for Bayesian multilevel models using
Stan. Journal of Statistical Software,
80(1), 1–28. https://doi.org/10.18637/jss.v080.i01
Bürkner, P.-C. (2018). Advanced Bayesian multilevel
modeling with the R package brms. The R Journal, 10(1),
395–411. https://doi.org/10.32614/RJ-2018-017
Bürkner, P.-C. (2024). brms:
Bayesian regression models using Stan. https://github.com/paul-buerkner/brms
Carl, S., & Baldwin, B. (2024). nflfastR: Functions to efficiently access
NFL play by play data. https://www.nflfastr.com/
Chakravarthy, P. (2012). Optimizing draft strategies in fantasy
football. https://harvardsportsanalysis.wordpress.com/wp-content/uploads/2012/04/fantasyfootballdraftanalysis1.pdf
Champely, S. (2020). pwr: Basic
functions for power analysis. https://github.com/heliosdrm/pwr
Chang, W. (2018). R graphics cookbook: Practical recipes for
visualizing data (2nd ed.). O’Reilly Media. https://r-graphics.org
Chatterjee, S. (2021). A new coefficient of correlation. Journal of
the American Statistical Association, 116(536), 2009–2022.
https://doi.org/10.1080/01621459.2020.1758115
Chatterjee, S., & Holmes, S. (2023). XICOR: Robust
and generalized correlation coefficients. https://CRAN.R-project.org/package=XICOR
Chekroud, A. (2017). nomogrammer: Fagan’s nomograms with ggplot2. https://github.com/achekroud/nomogrammer
Clark, K. (2018). The NFL’s analytics
revolution has arrived. https://www.theringer.com/2018/12/19/nfl/nfl-analytics-revolution
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers. https://doi.org/10.4324/9780203771587
Congelio, B. J. (2023). Introduction to NFL analytics
with R. CRC Press. https://bradcongelio.com/nfl-analytics-with-r-book
Corston, R., & Colman, A. M. (2000). A crash course in SPSS for
Windows. Wiley-Blackwell.
Critcher, C. R., & Rosenzweig, E. L. (2014). The performance
heuristic: A misguided reliance on past success when predicting
prospects for improvement. Journal of Experimental Psychology:
General, 143(2), 480–485. https://doi.org/10.1037/a0034129
Csárdi, G., Hester, J., Wickham, H., Chang, W., Morgan, M., &
Tenenbaum, D. (2024). remotes:
R package installation from remote repositories, including
GitHub. https://remotes.r-lib.org
D’Onofrio, B. M., Sjölander, A., Lahey, B. B., Lichtenstein, P., &
Öberg, A. S. (2020). Accounting for confounding in observational
studies. Annual Review of Clinical Psychology, 16(1),
25–48. https://doi.org/10.1146/annurev-clinpsy-032816-045030
Dana, J., & Thomas, R. (2006). In defense of clinical judgment … and
mechanical prediction. Journal of Behavioral Decision Making,
19(5), 413–428. https://doi.org/10.1002/bdm.537
Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus
actuarial judgment. Science, 243(4899), 1668–1674. https://doi.org/10.1126/science.2648573
Delignette-Muller, M. L., & Dutang, C. (2015). fitdistrplus: An R package for
fitting distributions. Journal of Statistical Software,
64(4), 1–34. https://doi.org/10.18637/jss.v064.i04
Delignette-Muller, M.-L., Dutang, C., & Siberchicot, A. (2025).
fitdistrplus: Help to fit of a
parametric distribution to non-censored or censored data. https://lbbe-software.github.io/fitdistrplus/
Den Hartigh, R. J. R., Niessen, A. S. M., Frencken, W. G. P., &
Meijer, R. R. (2018). Selection procedures in sports:
Improving predictions of athletes’ future performance.
European Journal of Sport Science, 18(9), 1191–1198.
https://doi.org/10.1080/17461391.2018.1480662
Digitale, J. C., Martin, J. N., & Glymour, M. M. (2022). Tutorial on
directed acyclic graphs. Journal of Clinical Epidemiology,
142, 264–267. https://doi.org/10.1016/j.jclinepi.2021.08.001
Eastwell, P. (2014). Understanding hypotheses, predictions, laws, and
theories. Science Education Review, 13(1), 16–21. https://eric.ed.gov/?id=EJ1057150
Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine:
Problems and opportunities. In D. Kahneman, P. Slovic, & A. Tversky
(Eds.), Judgment under uncertainty: Heuristics and biases (pp.
249–267). Cambridge University Press. https://doi.org/10.1017/CBO9780511809477.019
Enke, B. (2020). What you see is all there is. The Quarterly Journal
of Economics, 135(3), 1363–1398. https://doi.org/10.1093/qje/qjaa012
Fantasy Sports & Gaming Association. (2023). Industry
demographics. https://thefsga.org/industry-demographics/
Farrington, D. P., & Loeber, R. (1989). Relative improvement over
chance (RIOC) and phi as measures of predictive efficiency
and strength of association in 2×2 tables. Journal of Quantitative
Criminology, 5(3), 201–213. https://doi.org/10.1007/BF01062737
Fowler, J. (2015). Why the Steelers hired a
Carnegie Mellon professor for advanced analytics. https://www.espn.com/blog/pittsburgh-steelers/post/_/id/14521/why-the-steelers-hired-a-carnegie-mellon-professor-for-advanced-analytics
Fox, L. (2021). How the NFL uses analytics, according
to the lead analyst of a Super Bowl champion. https://www.forbes.com/sites/liamfox/2021/08/12/how-the-nfl-uses-analytics-according-to-the-lead-analyst-of-a-super-bowl-champion
Fraley, C., Raftery, A. E., & Scrucca, L. (2024). mclust: Gaussian mixture modelling for model-based
clustering, classification, and density estimation. https://mclust-org.github.io/mclust/
Free, H., Groenewold, M. R., & Luckhaupt, S. E. (2020). Lifetime
prevalence of self-reported work-related health problems among
US workers—United States, 2018. MMWR.
Morbidity and Mortality Weekly Report, 69(13), 361–365. https://doi.org/10.15585/mmwr.mm6913a1
Gabry, J., Češnovar, R., & Johnson, A. (2024). cmdstanr: R interface to
CmdStan. https://mc-stan.org/cmdstanr/
Gandrud, C. (2020). Reproducible research with R and
R studio (3rd ed.). CRC Press. https://www.routledge.com/Reproducible-Research-with-R-and-RStudio/Gandrud/p/book/9780367143985
Garb, H. N., & Wood, J. M. (2019). Methodological advances in
statistical prediction. Psychological Assessment,
31(12), 1456–1466. https://doi.org/10.1037/pas0000673
Garnier, S. (2024). viridis:
Colorblind-friendly color maps for R. https://sjmgarnier.github.io/viridis/
Garnier, S., Ross, N., Rudis, B., Sciaini, M., Camargo, A. P., &
Scherer, C. (2024). viridis(Lite) -
colorblind-friendly color maps for R. https://doi.org/10.5281/zenodo.4679423
Get Up ESPN. (2021). @nfldraftscout on
the Cowboys’ interest in drafting Kyle
Pitts. https://x.com/GetUpESPN/status/1380165126108672001
Getty, D., Li, H., Yano, M., Gao, C., & Hosoi, A. E. (2018). Luck
and the law: Quantifying chance in fantasy sports and other contests.
SIAM Review, 60(4), 869–887. https://doi.org/10.1137/16m1102094
Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in
basketball: On the misperception of random sequences. Cognitive
Psychology, 17(3), 295–314. https://doi.org/10.1016/0010-0285(85)90010-6
Goldschmied, N., Ratkovich, T., & Raphaeli, M. (in press). Brief
report: Exploring the icing the kicker strategy in the NFL. Journal
of Applied Sport Psychology. https://doi.org/10.1080/10413200.2024.2437166
Goldstein, J. (2013). Cat beats investors in stock market
challenge. https://www.npr.org/sections/money/2013/01/14/169326326/housecat-beats-investors-in-stock-market-challenge
Gonzalez Sanchez, A., Martinez, S., Yurko, R., Elmore, R., &
Macdonald, B. (2024). Beyond the box score: Does icing the field goal
kicker work in the NFL? CHANCE, 37(3), 41–48. https://doi.org/10.1080/09332480.2024.2415841
Goodman, S. (2008). A dirty dozen: Twelve p-value
misconceptions. Seminars in Hematology, 45(3),
135–140. https://doi.org/10.1053/j.seminhematol.2008.04.003
Goodman, Z. T., Casline, E., Jensen-Doss, A., Ehrenreich-May, J., &
Bainter, S. A. (2022). shinyDLRs: A
dashboard to facilitate derivation of diagnostic likelihood ratios.
Psychological Assessment, 34(6), 558–569. https://doi.org/10.1037/pas0001114
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of
informal (subjective, impressionistic) and formal (mechanical,
algorithmic) prediction procedures: The clinical–statistical
controversy. Psychology, Public Policy, and Law, 2(2),
293–323. https://doi.org/10.1037/1076-8971.2.2.293
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C.
(2000). Clinical versus mechanical prediction: A meta-analysis.
Psychological Assessment, 12(1), 19–30. https://doi.org/10.1037/1040-3590.12.1.19
Guo, J., Gabry, J., Goodrich, B., Johnson, A., Weber, S., & Badr, H.
S. (2024). rstan: R
interface to Stan. https://mc-stan.org/rstan/
Harrell, Jr., F. E. (2024). rms:
Regression modeling strategies. https://hbiostat.org/R/rms/
Harris, C. (2012). How to make VBD work for you.
https://www.espn.com/fantasy/football/ffl/story?page=nfldk2k12_vbdwork
Hitchings, J. (2012). Moneyball: Using modern portfolio theory to
win your fantasy sports league. https://eng.wealthfront.com/2012/01/17/moneyball-using-modern-portfolio-theory-to-win-your-fantasy-sports-league
Ho, T., & Carl, S. (2025a). Articles. https://nflreadr.nflverse.com/articles/index.html
Ho, T., & Carl, S. (2025b). Data dictionary - combine. https://nflreadr.nflverse.com/articles/dictionary_combine.html
Ho, T., & Carl, S. (2025c). Data dictionary - contracts. https://nflreadr.nflverse.com/articles/dictionary_contracts.html
Ho, T., & Carl, S. (2025d). Data dictionary - depth charts.
https://nflreadr.nflverse.com/articles/dictionary_depth_charts.html
Ho, T., & Carl, S. (2025e). Data dictionary - draft picks.
https://nflreadr.nflverse.com/articles/dictionary_draft_picks.html
Ho, T., & Carl, S. (2025f). Data dictionary - ESPN
QBR. https://nflreadr.nflverse.com/articles/dictionary_espn_qbr.html
Ho, T., & Carl, S. (2025g). Data dictionary - FF
opportunity. https://nflreadr.nflverse.com/articles/dictionary_ff_opportunity.html
Ho, T., & Carl, S. (2025h). Data dictionary - FF
player IDs. https://nflreadr.nflverse.com/articles/dictionary_ff_playerids.html
Ho, T., & Carl, S. (2025i). Data dictionary - FF
rankings. https://nflreadr.nflverse.com/articles/dictionary_ff_rankings.html
Ho, T., & Carl, S. (2025j). Data dictionary - FTN
charting. https://nflreadr.nflverse.com/articles/dictionary_ftn_charting.html
Ho, T., & Carl, S. (2025k). Data dictionary - injuries. https://nflreadr.nflverse.com/articles/dictionary_injuries.html
Ho, T., & Carl, S. (2025l). Data dictionary - next gen
stats. https://nflreadr.nflverse.com/articles/dictionary_nextgen_stats.html
Ho, T., & Carl, S. (2025m). Data dictionary -
participation. https://nflreadr.nflverse.com/articles/dictionary_participation.html
Ho, T., & Carl, S. (2025n). Data dictionary -
PBP. https://nflreadr.nflverse.com/articles/dictionary_pbp.html
Ho, T., & Carl, S. (2025o). Data dictionary - PFR passing.
https://nflreadr.nflverse.com/articles/dictionary_pfr_passing.html
Ho, T., & Carl, S. (2025p). Data dictionary - player stats.
https://nflreadr.nflverse.com/articles/dictionary_player_stats.html
Ho, T., & Carl, S. (2025q). Data dictionary - player stats
defense. https://nflreadr.nflverse.com/articles/dictionary_player_stats_def.html
Ho, T., & Carl, S. (2025r). Data dictionary - rosters. https://nflreadr.nflverse.com/articles/dictionary_rosters.html
Ho, T., & Carl, S. (2025s). Data dictionary - schedules. https://nflreadr.nflverse.com/articles/dictionary_schedules.html
Ho, T., & Carl, S. (2025t). Data dictionary - snap counts.
https://nflreadr.nflverse.com/articles/dictionary_snap_counts.html
Hoch, S. J. (1985). Counterfactual reasoning and accuracy in predicting
personal events. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 11(4), 719–731. https://doi.org/10.1037/0278-7393.11.1-4.719
Holmes, S., & Chatterjee, S. (2023). XICOR:
Association measurement through cross rank increments. https://CRAN.R-project.org/package=XICOR
Hopper, T. (2014). Can we do better than r-squared? https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared
Hough, S. E. (2016). Predicting the unpredictable: The tumultuous
science of earthquake prediction. Princeton University Press.
Hyndman, R. J. (2014). Alternative to MAPE when the
data is not a time series. https://stats.stackexchange.com/a/108963/20338
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting:
Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3
Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay,
L., Kuroptev, K., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang,
E., & Yasmeen, F. (2024). forecast:
Forecasting functions for time series and linear models. https://pkg.robjhyndman.com/forecast/
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series
forecasting: The forecast package for
R. Journal of Statistical Software,
27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
Iacobucci, D., Schneider, M. J., Popovich, D. L., & Bakamitsos, G.
A. (2016). Mean centering helps alleviate “micro” but not
“macro” multicollinearity. Behavior Research
Methods, 48(4), 1308–1317. https://doi.org/10.3758/s13428-015-0624-x
International Society of Genetic Genealogy. (2022). Autosomal DNA
statistics. https://isogg.org/wiki/Autosomal_DNA_statistics
Jackson-Wood, M. (2017). statistical test
flowchart. https://www.statsflowchart.co.uk
Jak, S., Jorgensen, T. D., Verdam, M. G. E., Oort, F. J., & Elffers,
L. (2020). Analytical power calculations for structural equation
modeling: A tutorial and shiny app. Behavior Research Methods.
https://doi.org/10.3758/s13428-020-01479-0
Johnson, J. E. V., & Bruce, A. C. (2001). Calibration of subjective
probability judgments in a naturalistic setting. Organizational
Behavior and Human Decision Processes, 85(2), 265–290. https://doi.org/10.1006/obhd.2000.2949
Jones, J. M. (2024). Football retains dominant position as favorite
U.S. sport. https://news.gallup.com/poll/610046/football-retains-dominant-position-favorite-sport.aspx
Kahneman, D. (2011). Thinking, fast and slow. Farrar,
Straus, and Giroux.
Kassambara, A. (2017). Practical guide to cluster analysis in
R: Unsupervised machine learning (Vol.
1). Sthda.
Keren, G. (1987). Facing uncertainty in the game of bridge: A
calibration study. Organizational Behavior and Human Decision
Processes, 39(1), 98–114. https://doi.org/10.1016/0749-5978(87)90047-1
Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M., &
Zubizarreta, J. R. (2020). Suicide prediction models: A critical review
of recent research with recommendations for the way forward.
Molecular Psychiatry, 25(1), 168–179. https://doi.org/10.1038/s41380-019-0531-0
Kievit, R., Frankenhuis, W., Waldorp, L., & Borsboom, D. (2013).
Simpson’s paradox in psychological science: A practical guide.
Frontiers in Psychology, 4(513). https://doi.org/10.3389/fpsyg.2013.00513
Kilin, I. (2022). The best charts for color blind viewers. https://www.datylon.com/blog/data-visualization-for-colorblind-readers
Koehler, D. J., Brenner, L., & Griffin, D. (2002). The calibration
of expert judgment: Heuristics and biases beyond the laboratory. In T.
Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and
biases: The psychology of intuitive judgment. Cambridge University
Press. https://doi.org/10.1017/CBO9780511808098.041
Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for
confidence. Journal of Experimental Psychology: Human Learning and
Memory, 6(2), 107–118. https://doi.org/10.1037/0278-7393.6.2.107
Kotrba, V. (2020). Heuristics in fantasy sports: Is it profitable to
strategize based on favourite of the match? Mind & Society,
19(1), 195–206. https://doi.org/10.1007/s11299-020-00231-7
Kuznetsova, A., Bruun Brockhoff, P., & Haubo Bojesen Christensen, R.
(2020). lmerTest: Tests in linear mixed
effects models. https://github.com/runehaubo/lmerTestR
Larrick, R. P., Mannes, A. E., & Soll, J. B. (2024). The social
psychology of the wisdom of crowds (with a new section on recent
advances). In F. M. Federspiel, G. Montibeller, & M. Seifert (Eds.),
Behavioral decision analysis (pp. 121–143). Springer. https://doi.org/10.1007/978-3-031-44424-1_7
Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall,
R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A.
R., Stewart, P. W., Suissa, S., Adjei, A. A., Akdis, C. A., Azoulay, É.,
Bakker, J., Ballas, Z. K., Bardin, P. G., Barreiro, E., … Vincent, J.-L.
(2019). Control of confounding and reporting of results in causal
inference studies. Guidance for authors from editors of respiratory,
sleep, and critical care journals. Annals of the American Thoracic
Society, 16(1), 22–28. https://doi.org/10.1513/AnnalsATS.201808-564PS
Lee, M. D., & Liu, S. (2022). Drafting strategies in fantasy
football: A study of competitive sequential human decision making.
Judgment and Decision Making, 17(4), 691–719. https://doi.org/10.1017/S1930297500008901
Lenth, R. V. (2025). emmeans: Estimated
marginal means, aka least-squares
means. https://rvlenth.github.io/emmeans/
Lewis, M. (2009). The no-stats all-star. https://www.nytimes.com/2009/02/15/magazine/15Battier-t.html
Lilienfeld, S. O. (2007). Psychological treatments that cause harm.
Perspectives on Psychological Science, 2(1), 53–70. https://doi.org/10.1111/j.1745-6916.2007.00029.x
Lindhiem, O., Petersen, I. T., Mentch, L. K., & Youngstrom, E. A.
(2020). The importance of calibration in clinical psychology.
Assessment, 27(4), 840–854. https://doi.org/10.1177/1073191117752055
Long, J. D., & Teetor, P. (2019). R cookbook: Proven recipes for
data analysis, statistics, and graphics (2nd ed.). O’Reilly Media.
https://rc2e.com
Ly, N. (2015). The rules of American football -
EXPLAINED! (NFL). https://www.youtube.com/watch?v=Ddwp1HyEFRE
Lyons, B. D., Hoffman, B. J., Michel, J. W., & Williams, K. J.
(2011). On the predictive efficiency of past performance and physical
ability: The case of the National Football League.
Human Performance, 24(2), 158–172. https://doi.org/10.1080/08959285.2011.555218
Magnusson, K. (2013). Creating a typical textbook illustration of
statistical power using either ggplot or
base graphics. https://rpsychologist.com/creating-a-typical-textbook-illustration-of-statistical-power-using-either-ggplot-or-base-graphics
Magnusson, K. (2014). Understanding statistical power and
significance testing. https://rpsychologist.com/d3/nhst/
Magnusson, K. (2015). Distribution of p-values
when comparing two groups. https://rpsychologist.com/d3/pdist
Magnusson, K. (2020). Interpreting correlations. https://rpsychologist.com/correlation
Magnusson, K. (2021). Understanding p-values
through simulations. https://rpsychologist.com/pvalue
Makridakis, S., Hogarth, R. M., & Gaba, A. (2009). Forecasting and
uncertainty in the economic and business world. International
Journal of Forecasting, 25(4), 794–812. https://doi.org/10.1016/j.ijforecast.2009.05.012
Mannes, A. E., Soll, J. B., & Larrick, R. P. (2014). The wisdom of
select crowds. Journal of Personality and Social Psychology,
107(2), 276–299. https://doi.org/10.1037/a0036677
Massey, C., & Thaler, R. H. (2013). The loser’s curse: Decision
making and market efficiency in the National Football
League draft. Management Science, 59(7),
1479–1495. https://doi.org/10.1287/mnsc.1120.1657
Mathieu, J. E., Aguinis, H., Culpepper, S. A., & Chen, G. (2012).
Understanding and estimating the power to detect cross-level interaction
effects in multilevel modeling. Journal of Applied Psychology,
97(5), 951–966. https://doi.org/10.1037/a0028380
Mazerolle, M. J. (2025). AICcmodavg: Model selection
and multimodel inference based on (Q)AIC(c). https://CRAN.R-project.org/package=AICcmodavg
McGrath, R. E., & Meyer, G. J. (2006). When effect sizes disagree:
The case of r and d. Psychological Methods,
11(4), 386–401. https://doi.org/10.1037/1082-989X.11.4.386
Meehl, P. E. (1957). When shall we use our heads instead of the formula?
Journal of Counseling Psychology, 4(4), 268–273. https://doi.org/10.1037/h0047554
Meehl, P. E. (1978). Theoretical risks and tabular asterisks:
Sir Karl, Sir
Ronald, and the slow progress of soft psychology.
Journal of Consulting and Clinical Psychology, 46(4),
806–834. https://doi.org/10.1037/0022-006x.46.4.806
Meehl, P. E. (1986). Causes and effects of my disturbing little book.
Journal of Personality Assessment, 50(3), 370–375. https://doi.org/10.1207/s15327752jpa5003_6
Meehl, P. E., & Rosen, A. (1955). Antecedent probability and the
efficiency of psychometric signs, patterns, or cutting scores.
Psychological Bulletin, 52(3), 194–216. https://doi.org/10.1037/h0048070
Miller, J. B., & Sanjurjo, A. (2014). A cold shower for the hot hand
fallacy. Innocenzo Gasparini Institute for Economic Research.
https://repec.unibocconi.it/igier/igi/wp/2014/518.pdf
Miller, J. B., & Sanjurjo, A. (2024). A cold shower for the hot hand
fallacy: Robust evidence from controlled settings. The Review of
Economics and Statistics, 106(6), 1607–1619. https://doi.org/10.1162/rest_a_01280
Miller, R. M. (2013). Cognitive bias in fantasy sports: Is your
brain sabotaging your team? Xlibris Press.
Mlodinow, L. (2008). The drunkard’s walk: How randomness rules our
lives. Pantheon Books.
Moore, D. A., & Healy, P. J. (2008). The trouble with
overconfidence. Psychological Review, 115(2), 502–517.
https://doi.org/10.1037/0033-295X.115.2.502
Morley, S. K., Brito, T. V., & Welling, D. T. (2018). Measures of
model performance based on the log accuracy ratio. Space
Weather, 16(1), 69–88. https://doi.org/10.1002/2017SW001669
Moshagen, M., & Bader, M. (2024). semPower: General power analysis
for structural equation models. Behavior Research Methods,
56(4), 2901–2922. https://doi.org/10.3758/s13428-023-02254-7
Moskowitz, T. J., & Wertheim, L. J. (2011). Scorecasting: The
hidden influences behind how sports are played and games are won.
Three Rivers Press.
Motz, B. (2013). Fantasy football: A touchdown for undergraduate
statistics education. Proceedings of the Games, Learning, and
Society Conference, 9.0, 222–228. https://doi.org/10.1184/R1/6686804.v1
Murayama, K., Usami, S., & Sakaki, M. (2022).
Summary-statistics-based power analysis: A new and practical method to
determine sample size for mixed-effects modeling. Psychological
Methods, 27(6), 1014–1038. https://doi.org/10.1037/met0000330
Murphy, A. H., & Winkler, R. L. (1984). Probability forecasting in
meterology. Journal of the American Statistical Association,
79(387), 489–500. https://doi.org/10.2307/2288395
NFL Fantasy Football. (2020). How to play fantasy football for
BEGINNERS. https://www.youtube.com/watch?v=XhrBapdhLEc
NFL Films Presents. (2014). Playing fantasy football for college
credit?? Welcome to C105 - Prediction, Probability,
& Pigskin. https://www.facebook.com/watch/?v=10155572257183615
Niles, B. (2022). Do players perform better in fantasy football in a
contract year? https://www.4for4.com/2022/preseason/do-players-perform-better-fantasy-football-contract-year
Nivison, A. (2021). Florida TE Kyle
Pitts draws comparison to Lebron James. https://247sports.com/article/kyle-pitts-lebron-james-2021-nfl-draft-florida-gators-football-163882176
Nuñez, J. R., Anderton, C. R., & Renslow, R. S. (2018). Optimizing
colormaps with consideration for color vision deficiency to enable
accurate interpretation of scientific data. PLOS ONE,
13(7), e0199239. https://doi.org/10.1371/journal.pone.0199239
NYT 4th Down Bot. (2014). 4th down: When to go for it and why.
https://www.nytimes.com/2014/09/05/upshot/4th-down-when-to-go-for-it-and-why.html
Oskamp, S. (1965). Overconfidence in case-study judgments. Journal
of Consulting Psychology, 29(3), 261–265. https://doi.org/10.1037/h0022125
Pelechrinis, K., & Winston, W. (2022). The hot hand in the wild.
PLOS ONE, 17(1), e0261890. https://doi.org/10.1371/journal.pone.0261890
Petersen, I. T. (2024). Principles of psychological assessment: With
applied examples in R. Chapman and
Hall/CRC. https://doi.org/10.1201/9781003357421
Petersen, I. T. (2025a). petersenlab: A
collection of R functions by the Petersen
Lab. https://doi.org/10.32614/CRAN.package.petersenlab
Petersen, I. T. (2025b). Principles of psychological assessment:
With applied examples in R. University of Iowa
Libraries. https://doi.org/10.25820/work.007199
Press, T. A. (2008). Janikowski gives Raiders win over
Jets in overtime. https://www.nfl.com/news/janikowski-gives-raiders-win-over-jets-in-overtime-09000d5d80bc3910
Pro Football Reference. (2024). 2024 NFL advanced stats. https://www.pro-football-reference.com/years/2024/advanced.htm
Pro Football Reference. (2025). About our advanced stats. https://www.pro-football-reference.com/about/advanced_stats.htm
R Core Team. (2024). R: A language and environment for
statistical computing. R Foundation for Statistical
Computing. https://www.R-project.org
Rader, C. A., Larrick, R. P., & Soll, J. B. (2017). Advice as a form
of social influence: Informational motives and the consequences for
accuracy. Social and Personality Psychology Compass,
11(8), e12329. https://doi.org/10.1111/spc3.12329
Reed, T. (2016). In an NFL divided over analytics,
Cleveland Browns look to make numbers add up in their
favor. https://www.cleveland.com/browns/2016/01/in_an_nfl_divided_over_analyti.html
Rice, M. E., Harris, G. T., & Lang, C. (2013). Validation of and
revision to the VRAG and SORAG: The
Violence Risk Appraisal Guide—Revised
(VRAG-R). Psychological Assessment,
25(3), 951–965. https://doi.org/10.1037/a0032878
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez,
J.-C., & Müller, M. (2011). pROC: An
open-source package for R and S+ to analyze
and compare ROC curves. BMC Bioinformatics,
12, 77. https://doi.org/10.1186/1471-2105-12-77
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez,
J.-C., & Müller, M. (2023). pROC:
Display and analyze ROC curves. https://xrobin.github.io/pROC/
Rohrer, J. M. (2018). Thinking clearly about correlations and causation:
Graphical causal models for observational data.
Advances in Methods and Practices in Psychological Science,
1(1), 27–42. https://doi.org/10.1177/2515245917745629
Romer, D. (2006). Do firms maximize? Evidence from professional
football. Journal of Political Economy, 114(2),
340–365. https://doi.org/10.1086/501171
Rosalsky, G. (2023). Should we invest more in weather forecasting?
It may save your life. https://www.npr.org/sections/money/2023/07/11/1186458991/should-we-invest-more-in-weather-forecasting-it-may-save-your-life
Rosenthal, G. (2018). Super Bowl LII: How the 2017
Philadelphia Eagles were built. https://www.nfl.com/news/super-bowl-lii-how-the-2017-philadelphia-eagles-were-built-0ap3000000912753
Russo, J. E., & Schoemaker, P. J. (1992). Managing overconfidence.
Sloan Management Review, 33(2), 7.
Ryan, J. (2013). Beating the NBA draft: Does any team
outperform expectations? https://harvardsportsanalysis.org/2013/11/beating-the-nba-draft-does-any-team-outperform-expectations
Ryan, J. A., & Ulrich, J. M. (2024). quantmod: Quantitative financial modelling
framework. https://www.quantmod.com/
Salmon, M. (2018). Where to get help with your R
question? https://masalmon.eu/2018/07/22/wheretogethelp/
Schalter, T. (2022). The NFL preseason is not
predictive — but it can often seem that way. https://fivethirtyeight.com/features/the-nfl-preseason-is-not-predictive-but-it-can-often-seem-that-way
Scherer, C. (2021). Beyond bar and box plots. https://z3tt.github.io/beyond-bar-and-box-plots
Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017). Determining
power and sample size for simple and complex mediation models.
Social Psychological and Personality Science, 8(4),
379–386. https://doi.org/10.1177/1948550617715068
Schwabish, J. (2021). Better data visualizations: A guide for
scholars, researchers, and wonks. Columbia University Press. https://doi.org/10.7312/schw19310
Schwartz, A. (2006). Diagnostic test calculator. http://araw.mede.uic.edu/cgi-bin/testcalc.pl
Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023).
Model-based clustering, classification, and density estimation using
mclust in R. Chapman;
Hall/CRC. https://doi.org/10.1201/9781003277965
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002).
Experimental and quasi-experimental designs for generalized causal
inference. Houghton Mifflin.
Sharpe, L. (2020a). NFL data sets. https://github.com/nflverse/nfldata/blob/master/DATASETS.md
Sharpe, L. (2020b). NFL data sets - draft values.
https://github.com/nflverse/nfldata/blob/master/DATASETS.md#draft_values
Sharpe, L. (2020c). NFL data sets - rosters. https://github.com/nflverse/nfldata/blob/master/DATASETS.md#rosters
Sharpe, L. (2020d). NFL data sets - standings. https://github.com/nflverse/nfldata/blob/master/DATASETS.md#standings
Sharpe, L. (2020e). NFL data sets - trades. https://github.com/nflverse/nfldata/blob/master/DATASETS.md#trades
Sherman, A., & Goldner, K. (2021). Sharpstack: Cholesky
correlations for building better lineups. https://assets-global.website-files.com/5f1af76ed86d6771ad48324b/607a4434a565aa7763bd1312_AndyAsh-Sharpstack-RPpaper.pdf
Sievert, C. (2020). Interactive web-based data visualization with
R, plotly, and shiny. Chapman; Hall/CRC. https://plotly-r.com
Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K.,
Corvellec, M., & Despouy, P. (2024). plotly: Create interactive web graphics via plotly.js. https://plotly-r.com
Signorell, A. (2025). DescTools: Tools for descriptive
statistics. https://andrisignorell.github.io/DescTools/
Silver, N. (2012). The signal and the noise: Why so many predictions
fail–but some don’t. Penguin.
Simoiu, C., Sumanth, C., Mysore, A., & Goel, S. (2019). Studying the
"wisdom of crowds" at scale. Proceedings of the AAAI Conference on
Human Computation and Crowdsourcing, 7(1), 171–179. https://doi.org/10.1609/hcomp.v7i1.5271
Skala, D. (2008). Overconfidence in psychology and finance–an
interdisciplinary literature review. Bank i Kredyt, 4,
33–50.
Smith, B., Sharma, P., & Hooper, P. (2006). Decision making in
online fantasy sports communities. Interactive Technology and Smart
Education, 3(4), 347–360. https://doi.org/10.1108/17415650680000072
Smith, G. (2016). The Sports Illustrated cover
jinx. https://www.psychologytoday.com/us/blog/what-the-luck/201610/the-sports-illustrated-cover-jinx
Spector, P. E., & Brannick, M. T. (2010). Methodological urban
legends: The misuse of statistical control variables.
Organizational Research Methods, 14(2), 287–305. https://doi.org/10.1177/1094428110369842
Spinu, V., Grolemund, G., & Wickham, H. (2024). lubridate: Make dealing with dates a little
easier. https://lubridate.tidyverse.org
Stack Overflow. (2018). How to make a great R
reproducible example. https://stackoverflow.com/a/5963610
Stack Overflow. (2025). How to create a minimal, reproducible
example. https://stackoverflow.com/help/minimal-reproducible-example
statistica. (2023a). Fantasy sports in the U.S.-
statistics & facts. https://www.statista.com/topics/10895/fantasy-sports-in-the-us/
statistica. (2023b). Most watched sports leagues in the United
States in 2023, by minutes watched. https://www.statista.com/statistics/1430289/most-watched-sports-leagues-usa/
Stevens, R. J., & Poppe, K. K. (2020). Validation of clinical
prediction models: What does the “calibration slope” really
measure? Journal of Clinical Epidemiology, 118, 93–99.
https://doi.org/10.1016/j.jclinepi.2019.09.016
Steyerberg, E. W., & Vergouwe, Y. (2014). Towards better clinical
prediction models: Seven steps for development and an ABCD for
validation. European Heart Journal, 35(29), 1925–1931.
https://doi.org/10.1093/eurheartj/ehu207
Strauss, M. E., & Smith, G. T. (2009). Construct validity: Advances
in theory and methodology. Annual Review of Clinical
Psychology, 5(1), 1–25. https://doi.org/10.1146/annurev.clinpsy.032408.153639
Surowiecki, J. (2005). The wisdom of crowds. Anchor Books.
Tetlock, P. E. (2017). Expert political judgment: How good is it?
How can we know? - New edition. Princeton University
Press.
Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., &
Ellison, G. T. (2016). Robust causal inference using directed acyclic
graphs: The r package ’dagitty’.
International Journal of Epidemiology, 45(6),
1887–1894. https://doi.org/10.1093/ije/dyw341
Tofallis, C. (2015). A better measure of relative prediction accuracy
for model selection and model estimation. Journal of the Operational
Research Society, 66(8), 1352–1362. https://doi.org/10.1057/jors.2014.103
TotalProSports.com. (2017). 10 most ridiculous things ever said by
Stephen A. Smith or Skip Bayless. https://www.youtube.com/watch?v=lTjBuEPcLlc
Treat, T. A., & Viken, R. J. (2023). Measuring test performance with
signal detection theory techniques. In H. Cooper, M. N. Coutanche, L. M.
McMullen, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA
handbook of research methods in psychology: Foundations, planning,
measures, and psychometrics (2nd ed., Vol. 1, pp. 837–858).
American Psychological Association. https://doi.org/10.1037/0000318-038
Tufte, E. R. (2001). The visual display of quantitative
information. Graphics Press.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty:
Heuristics and biases. Science, 185(4157), 1124–1131.
https://doi.org/10.1126/science.185.4157.1124
Underwood, A. (2019). 15 ways analytics has changed sports. https://stacker.com/stories/sports/15-ways-analytics-has-changed-sports
Ursenbach, J., O’Connell, M. E., Neiser, J., Tierney, M. C., Morgan, D.,
Kosteniuk, J., & Spiteri, R. J. (2019). Scoring algorithms for a
computer-based cognitive screening tool: An illustrative example of
overfitting machine learning approaches and the impact on estimates of
classification accuracy. Psychological Assessment,
31(11), 1377–1382. https://doi.org/10.1037/pas0000764
Wagner, C., & Vinaimont, T. (2010). Evaluating the wisdom of crowds.
Issues in Information Systems, 11(1), 724–732. http://iacis.org/iis/2010/724-732_LV2010_1546.pdf
Walder, S. (2020). 2020 NFL analytics survey: Which
teams are most, least analytically inclined? https://www.espn.com/nfl/story/_/id/29939438/2020-nfl-analytics-survey-which-teams-most-least-analytically-inclined
Wang, Y. A., & Rhemtulla, M. (2021). Power analysis for parameter
estimation in structural equation modeling: A discussion and tutorial.
Advances in Methods and Practices in Psychological Science,
4(1), 1–17. https://doi.org/10.1177/2515245920918253
Wetzels, R., Tutschkow, D., Dolan, C., Sluis, S. van der, Dutilh, G.,
& Wagenmakers, E.-J. (2016). A Bayesian test for the
hot hand phenomenon. Journal of Mathematical Psychology,
72, 200–209. https://doi.org/10.1016/j.jmp.2015.12.003
White, M. H., & Sheldon, K. M. (2014). The contract year syndrome in
the NBA and MLB: A classic
undermining pattern. Motivation and Emotion, 38(2),
196–205. https://doi.org/10.1007/s11031-013-9389-7
Wickham, H. (2023). tidyverse: Easily
install and load the Tidyverse. https://tidyverse.tidyverse.org
Wickham, H. (2024). ggplot2: Elegant graphics for data analysis
(3rd ed.). Springer. https://ggplot2-book.org
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D.,
François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M.,
Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J.,
Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to
the tidyverse. Journal of Open Source
Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K.,
Wilke, C., Woo, K., Yutani, H., Dunnington, D., & van den Brand, T.
(2024). ggplot2: Create elegant data
visualisations using the grammar of graphics. https://ggplot2.tidyverse.org
Wilke, C. O., & Wiernik, B. M. (2022). ggtext: Improved text rendering support for ggplot2. https://wilkelab.org/ggtext/
Williams, A. J., Botanov, Y., Kilshaw, R. E., Wong, R. E., &
Sakaluk, J. K. (2021). Potentially harmful therapies: A meta-scientific
review of evidential value. Clinical Psychology: Science and
Practice, 28(1), 5–18. https://doi.org/10.1111/cpsp.12331
Wood, S. N. (2017). Generalized additive models: An introduction
with R (2nd ed.). CRC press. https://doi.org/10.1201/9781315370279
Wood, S. N. (2023). mgcv: Mixed
GAM computation vehicle with automatic smoothness
estimation. https://doi.org/10.32614/CRAN.package.mgcv
Woodland, L. M., & Woodland, B. M. (2015). The National
Football League season wins total betting market: The impact of
heuristics on behavior. Southern Economic Journal,
82(1), 38–54. https://doi.org/10.4284/0038-4038-2013.145
Wuertz, D., Setz, T., Chalabi, Y., & Theussl, S. (2023). fPortfolio: Rmetrics - portfolio
selection and optimization. https://r-forge.r-project.org/projects/rmetrics/
Wysocki, A. C., Lawson, K. M., & Rhemtulla, M. (2022). Statistical
control requires causal justification. Advances in Methods and
Practices in Psychological Science, 5(2),
25152459221095823. https://doi.org/10.1177/25152459221095823
Xie, Y., Dervieux, C., & Riederer, E. (2020). R
Markdown cookbook. CRC Press. https://bookdown.org/yihui/rmarkdown-cookbook
Xie, Y., Dervieux, C., & Riederer, E. (2024). R markdown
cookbook. CRC Press. https://bookdown.org/yihui/rmarkdown-cookbook
Yahoo! Sports. (2024). How cognitive bias affects your fantasy draft
strategy with neuroscience professor Dr. Renee Miller.
https://www.youtube.com/watch?v=gmpLFWs5ae0
Yutani, H. (2023). gghighlight:
Highlight lines and points in ggplot2.
https://yutannihilation.github.io/gghighlight/
Zhang, Z., & Yuan, K.-H. (2018). Practical statistical power
analysis using WebPower and R. ISDSA
Press. https://doi.org/10.35566/power