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Jordan Love Was The Right Pick In Theory

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Let me preface this with NFL assets are ALWAYS risks. You need evaluate such in a probabilistic perspective, and that includes looking at every outcome to generate an expectation. The Love pick has a positive expected value (likely, without personally computing such), and teams should always pursue positive expected values for the QB position. Furthermore, this is the right strategy, even if it does not pay off for the Packers. The pick for Jordan love is very much the right type of strategy for a team to follow in finding a succession plan for a franchise QB. The worst position for a team to find a QB is stuck in the 15-30 range of draft position, never being able to grab a top 3 QB in a draft. Instead, the Packers chose to take a riskier prospect, give themselves time to develop and further project this player, and they have a chance to move on and consider an alternative route if he does not work out. The availability of an inconsistent prospect with elite highlights is akin to t
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Profiling 2019 NFL Offenses with nflscrapR Data and Clustering

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Project Overview: I used nflscrapR 2019 season data to organize teams' playcalling in normalized relative proportions, then used a clustering algorithm to categorize each team into one of five groups. Analysis and Report by Kevin Kraege found at @kevgk2 on Twitter Cluster numbers were generated arbitrarily, and not according to specific order of clustering. It may seem odd to refer to teams by their cities instead of team names, but I tried to remain consistent between my analysis and how the data is charted and graphed. For this project, I tend to gloss over the details of the mathematics and methods used, such as principal components, agglomerative clustering, silhouette method, or EPA. I advise you to research these yourself to find someone more qualified to explain them. Contact me on twitter if you wish for some help doing so. Data Specifications: Each play recorded fit into one of 12 groups. These groups are combinations of: three types of runs (end, guard, tackle),
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Rstudio code for MWP

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You can read about this project at https://comfortablynumb-ers.blogspot.com/2020/02/win-probability-analysis-part-1-mean.html follow me at @kevgk2 on Twitter check out more rstudio guides and nflscrapR links at the bottom #legend: #pbp: play by play #WP: win probability #MWP: average win probability #WPE: win probability expected wins #pyth: pythagorean wins #install packages install.packages("nflscrapr") install.packages("tidyverse") install.packages("lubridate") install.packages("na.tools") install.packages("ggimage") #load libraries library(nflscrapR) library(tidyverse) library(lubridate) library(na.tools) library(ggimage) #determining the mean WP for a particular game #scrape games week_14_games <- scrape_game_ids(2019, weeks = 14) #pull play by play for specific game Gb_Was_PbP <- week_14_games %>%   filter(home_team == "GB") %>%   pull(game_id) %>%   scrape_json_play_by_pl
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Win Probability Analysis part 1: Mean Win Probability

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Win Probability Analysis part 1: Mean Win Probability Win Probability Analysis part 2: Win Probability Added Win Probability Analysis part 3: MWP Modified Check out the posts of R code after each release! Link at bottom, along with more nflscrapR and RStudio guides. Context: This project is about the Win Probability (WP) metric from the nflscrapR package in RStudio. This is a logistic regression that predicts win probability given some in game statistics. The data, collected by the nflscrapR, is from the NFL. All I did is average the WP calculation for every point in every game, and threw it on some plots. Did I invent Mean Win Probability? Not even close. Am I the first to consider it? Very unlikely. Despite that, I have not seen anything about it except here (somewhat): https://operations.nfl.com/stats-central/stats-articles/win-probability-models-for-every-nfl-team-in-2019/ Here is some literature about the WP calculations: https://arxiv.org/pdf/1802.00998.pdf https
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