Debunking common NFL myths in an analytical study on the true value of passing the ball

在關于傳球真實價值的分析研究中揭穿NFL常見神話

Background

背景

Analytics are not used enough in the NFL. In a league with an abundance of money, intelligence, and skill, one may assume that the game we see teams play today is the most optimized and efficient it can be. However, the reality is that the league and its gurus still stick to many traditional aspects of the sport, not utilizing analytical techniques as much as they could. Our research reveals a significant flaw in the way the game is currently played.

在NFL中，分析使用不足。 在一個擁有大量金錢，智力和技能的聯賽中，人們可能會認為我們今天看到的球隊所進行的比賽是最優化，最高效的。 但是，現實情況是，聯盟及其專家仍然堅持這項運動的許多傳統方面，沒有盡可能多地利用分析技術。 我們的研究表明，目前的游戲方式存在重大缺陷。

Around the same time the three-point line was added to the NBA, the NFL experienced a similarly drastic change with the popularization of the Spread Offense. Many know it as the change that was designed to spread the opposing team’s defense horizontally across the field, exposing holes for the offense. A lesser-known fact is that the implementation of the playstyle caused the passing game to be more efficient than ever…

大約在三分線加入NBA的同時，隨著Spread Offense的普及，NFL經歷了類似的急劇變化。 很多人都知道它作為被設計為橫向傳播對方球隊的防守穿過田野，露出了犯罪Kong的變化。 鮮為人知的事實是，游戲風格的實現使傳球游戲比以往任何時候都更有效率。

… and increasingly so over the years. Figure 1 clarifies that the efficiency (which accounts for yards, first downs, touchdowns, interceptions, and sacks) for passing has for at least the past forty years significantly exceeded rush efficiency.

…并且在過去幾年中越來越多。 圖1闡明，至少過去四十年來，通過的效率(考慮到碼數，首次起落，觸地得分，攔截和麻袋)已經大大超過了沖刺效率。

Initial Findings & Suspicions

初步調查結果和懷疑

Mind-blown by this widening chasm between the efficiencies of the two play types, we set out to explore the significance of this discrepancy and how it has affected modern NFL teams’ decision-making. For the data source, NFL play-by-play data from Ron Yurko’s nflscrapR package in R was aggregated.

由于兩種比賽效率之間的差距不斷擴大，我們對此感到震驚，我們著手探討這種差異的重要性以及它如何影響現代NFL球隊的決策。 對于數據源，Ron Yurko在R中的nflscrapR程序包中的NFL逐次播放數據被匯總。

The initial findings supported the idea that passing has become more efficient — in 2019, the average yards gained from a pass attempt (PYPA) was 6.73 while the average yards gained from carries (YPC) was 4.40. The Baltimore Ravens, who averaged the most yards per carry (5.6), averaged less YPC than Mitch Trubisky and the Chicago Bears’ dismal 5.7 PYPA (worst in the 2019 season). Nonetheless, as seen in Figures 2 & 3, NFL teams have only passed the football 58.5% of the time (out of run or pass plays only) over the past eleven seasons, a stat that one would logically expect to be greater given the above numbers.

最初的調查結果支持傳球變得更加有效的想法-在2019年，傳球嘗試(PYPA)獲得的平均碼數為6.73，而傳球(YPC)獲得的平均碼數為4.40。 巴爾的摩烏鴉的平均每碼平均碼數最多(5.6)，其平均YPC值低于米奇·特魯比斯基和芝加哥熊隊慘淡的5.7 PYPA(2019賽季最差)。 盡管如此，如圖2和3所示，在過去的11個賽季中，NFL球隊僅通過了58.5％的時間(僅是失控或傳球)，這在邏輯上是一個合理的數字數字。

If teams only pass 58.5% of the time even after the best rushing teams fail to average more YPC than the league’s worst passing team’s PYPA, then surely, there must be something missing. Though not necessarily flawed, the initial findings offered a rather one-dimensional analysis of the data. While it was concluded that passing yielded significantly more yards for any team, it had still not been confirmed that passing was more correlated with success. Therefore, the group sought to compare the correlations between teams’ winning percentages (WP) in a given season and rushing and passing, respectively. First, a metric was required to measure the success of each play type for all 352 teams (32 teams * 11 seasons). The most logical choice was using Success Rate over Average. A play is considered to be successful when it gains at least 40% of yards-to-go on first down, 60% of yards-to-go on second down, and 100% of yards-to-go on third or fourth down. The metric was calculated by taking a team’s average success rate of a play type and dividing it by the league average for the given season. The correlations were then run between the success rates and the WPs and graphed, as seen below.

如果即使在最好的沖鋒隊的平均YPC不能超過聯盟最差的沖鋒隊的PYPA之后，如果團隊僅在58.5％的時間上通過了，那么，毫無疑問，肯定有一些不足。 盡管不一定有缺陷，但最初的發現提供了對數據的一維分析。 盡管可以得出結論，傳球可以為任何球隊帶來更多的碼數，但仍不能確定傳球與成功的關系更大。 因此，該小組尋求比較給定季節中各隊的獲勝率(WP)與沖刺和傳??球之間的相關性。 首先，需要一個度量標準來衡量所有352支球隊(32支球隊* 11個賽季)每種比賽類型的成功率。 最合乎邏輯的選擇是使用成功率高于平均水平。 如果一局比賽在第一局下降時獲得至少40％的碼數，在第二局下降時獲得至少60％的碼數，而在第三局或第四局下降時獲得100％的碼數，則視為成功。 。 該度量標準是通過計算球隊在某一賽季的平均成功率并將其除以聯賽平均水平得出的。 然后，在成功率和WP之間運行相關性并繪制圖表，如下所示。

Although neither variable has a strong correlation (> .7) with WP, passing is nearly twice as correlated with winning than rushing. Even without knowing the correlations, one can infer that passing success is more correlated with winning merely by noticing the spread of the data points on the graphs. While the comparison in Figure 4 has outliers well beyond the reaches of a wide oval shape, Figure 5 contains all of its data within a football-shaped ellipse.

盡管兩個變量都不與WP有很強的相關性(> .7)，但傳球與獲勝的相關性幾乎是沖刺的兩倍。 即使不知道相關性，也可以僅通過注意到圖上數據點的分布來推斷傳遞成功與獲勝更相關。 盡管圖4中的比較具有超出寬橢圓形范圍之外的異常值，但圖5包含了其所有數據，這些數據都位于橄欖球形橢圓內。

Creating a Win Percentage Model

創建贏率百分比模型

The next step in the process was constructing a model that predicted a team’s winning percentage given certain passing and rushing attributes. This model would identify which aspects of a team were more important in creating a successful team — this meant selecting rushing and passing variables and weighing them to optimize the model’s correlation with WP. The variables used were the following:

該過程的下一步是構建一個模型，該模型在給定傳球和沖刺屬性的情況下預測球隊的獲勝百分比。 該模型將確定團隊的哪些方面對于創建一個成功的團隊更重要-這意味著選擇緊急和傳遞變量并權衡它們，以優化模型與WP的相關性。 使用的變量如下：

Passing Attributes:

傳遞屬性：

• Passing Success Rate (as described before)
  通過成功率(如上所述)
• Adjusted Sack Rate on pass plays (inversely proportional to a team’s success in pass protection)
  調整傳球后的解雇率(與球隊傳球保護的成功成反比)
• Pass Touchdowns (directly proportional to pass scoring and success)
  傳球達陣(與傳球得分和成功成正比)

Rushing Attributes:

沖屬性：

• Rushing Success Rate
  沖動成功率
• Adjusted Line Yards on rushing plays (directly proportional to a team’s success in rush blocking)
  搶斷調整后的線碼(與球隊成功搶斷成正比)
• Rush Touchdowns
  緊急達陣

A team legend was created so the outliers could be identified.

創建了團隊圖例，以便可以識別異常值。

With a correlation of about 0.67, the refined model in Figure 6 came close to a strong relationship with WP. More significantly, passing statistics had an effect on the model that was 1.5 times greater than rushing stats — in other words, the pass variables were weighed 1.5x more. With this compelling evidence, it was becoming more apparent that the passing game is far more critical for a team’s success than its running game. The increasingly confirmed hypothesis that teams do not pass enough was now raising more questions concerning NFL teams’ decision-making.

由于相關性約為0.67，圖6中的精煉模型與WP緊密相關。 更重要的是，傳遞統計信息對模型的影響比緊急統計信息大1.5倍-換句話說，傳遞變量的權重比其他統計數據高1.5倍。 有了這些令人信服的證據，越來越明顯的是，傳球對團隊的成功至關重要，而不是奔跑的游戲。 越來越多的關于團隊沒有通過足夠多的假設的假說現在正引發更多有關NFL團隊決策的問題。

Busting Two Common Myths in the Modern NFL

打破現代NFL中的兩個常識

Surely there was something still missing — a key factor not taken into account was the potential drawback of repetition and its effect on the value of a play in a game. A common notion about play-calling is that repetition tends to decrease the value of a play while the potential unpredictability in “changing it up” (running/passing almost the same number of times) keeps the defense on their feet. To measure the value of variability — or loss of value with repetition — comparisons were run between the run/pass proportions of past play-calls in a game and the EPA of the current play, as seen in Figures 7 & 8. EPA, or expected points added, is a popular metric used to quantify the value of a play in terms of the number of points it is predicted to yield for the team with the ball. As ESPN explains, “without going into technical details, the key is that the relationships in the EP formula encapsulate the basic tenets of football, including: being closer to the opposing goal line and farther from your own is better; earlier downs are better (first-and-10 is better than second-and-10, etc.); shorter distance to go is better; being at home is better.” To study this comparison, all NFL plays since the 2009 season were used.

當然，仍然缺少一些東西–一個未被考慮的關鍵因素是重復的潛在缺點及其對游戲價值的影響。 關于打出電話的一個普遍觀念是，重復往往會降低打出的球的價值，而“改變”(運行/傳球次數幾乎相同)中潛在的不可預測性卻使他們的防守更加穩固。 為了衡量可變性的價值或因重復而造成的價值損失，在游戲中過去玩過的通話次數與當前比賽的EPA的通過/通過比例之間進行了比較，如圖7和8所示。預期得分是一種流行的度量標準，用于根據預測為帶球球隊帶來的得分數來量化比賽的價值。 正如ESPN所解釋的那樣，“無需贅述技術細節，關鍵在于EP公式中的關系囊括了足球的基本宗旨，包括：越接近對方的球門線，越遠越好； 越早越好(第一和十比第二和十更好，依此類推)； 距離越短越好； 在家比較好。” 為了研究這種比較，使用了2009賽季以來的所有NFL比賽。

To reiterate, the x-axes represent the percent of previous plays in a given game that was either run or pass, and the y-axes represent the value of the given play. There are many data points at a value of 100% of previous plays being the same play because this only occurs in the first few plays of a game when there is no variation. It is also important to note that “blowout” games of wins by more than four possessions were excluded — since teams tend to pass a lot in desperation, causing those plays to be far less successful. The clear takeaway from Figures 7 & 8 is that repeating the same play type throughout a game does not have any impact on its EPA since the blue line of best fit has a slope of < 0.01. Something else to emphasize from these charts is the reason why these many data points were left to display, which is the fact that there are far more points toward the middle of the graph where about half of the past plays are the same play call. What this shows is the flawed concept in the NFL that there should be an even mix of plays.

重申一下，x軸表示在給定游戲中已運行或通過的先前游戲的百分比，y軸表示給定游戲的值。 有許多數據點，前一個游戲的100％的值是相同的游戲，因為這只發生在游戲的前幾個游戲中且沒有變化時。 還需要注意的是，排除了超過四個回合贏得“井噴式”比賽的原因-因為球隊在絕望中往往會傳球很多，導致這些比賽的成功率要差得多。 從圖7和8中的清楚的外賣是，在整個游戲重復相同的播放類型沒有因為最佳擬合的藍線具有<0.01的斜率在其EPA任何影響。 這些圖表中還有其他要強調的地方是要顯示這些數據點的原因，這是事實，在圖表的中間有更多的點，過去的比賽中有大約一半是相同的比賽。 這說明NFL中存在一個有缺陷的概念，那就是應該有均勻的比賽組合。

Furthermore, the same graph functions were run but for the proportion of past play calls being the opposite play type — increasing previous run percentages compared to pass EPA and vice versa.

此外，運行了相同的圖形函數，但是過去的播放調用的比例是相反的播放類型-與通過EPA相比，增加了之前的運行百分比，反之亦然。

The change in EPA was similarly negligible in these cases as well. All trends were consistent throughout the past eleven seasons. The percentages are simply one minus the percentages from the previous two graphs but are shown here to emphasize that the change in EPA is negligible in these cases as well. It can reasonably be concluded from Figures 7–10 that passing does not lose any value even if teams have already called a high percentage of passes in a given game. This debunks the common myth of the importance of establishing the run, which — as mentioned briefly earlier — is the idea that teams must run the ball to keep the defense honest. From the above analysis, there is no apparent reason why teams do not throw the ball more. But exactly how much would teams benefit if they were to call more pass plays? To quantify this value for any team is difficult since there has been no team who has experimented with throwing the ball exceedingly more.

在這些情況下，EPA的變化同樣可以忽略不計。 在過去的十一個季節中，所有趨勢都是一致的。 百分比只是前兩個圖的百分比減去一個百分比，但此處顯示的是要強調的是，在這些情況下，EPA的變化也可以忽略不計。 從圖7-10可以合理地得出結論，即使團隊已經在給定比賽中要求很高的傳球率，傳球也不會損失任何價值。 這打破了建立奔跑的重要性的普遍神話，正如前面簡短提到的那樣，這是團隊必須為保持誠實的防守而奔波的想法。 通過以上分析，沒有明顯的理由可以解釋為什么球隊不多丟球。 但是，如果球隊打出更多的傳球機會，他們究竟會從中受益多少呢？ 對于任何一支球隊來說，量化這個價值都是困難的，因為沒有任何一支球隊嘗試過將球投得更多。

Quantifying the Value of Passing More Often

量化通過的價值

It was first necessary to compare teams’ mean pass EPAs with their run EPAs. Success rates (used earlier in the study) would not be useful in quantifying the value of passing because they only provide the percentage of times that teams are successful and not the actual value of a play.

首先需要將團隊的平均通過EPA與他們的運行EPA進行比較。 成功率(在研究的早期使用)在量化傳球的價值上沒有用，因為它們僅提供球隊成功的次數百分比，而不是比賽的實際價值 。

Mean League Pass EPA = 0.0517 | Mean League Run EPA = -0.0229

平均聯賽通行證EPA = 0.0517 | 聯賽平均EPA = -0.0229

In Figure 11, the discrepancies between teams’ mean pass and run EPAs are clear — on average, 2019 regular season teams’ pass plays yielded 0.0746 more expected points than run plays. Not only is passing the ball far more effective but running it is also losing teams potential points.

在圖11中，團隊的平均傳球次數與跑步EPA之間的差異很明顯-平均而言，2019年常規賽季球隊的傳球次數比跑步次數多0.0746預期點。 傳球不僅效率更高，而且奔跑還會失去球隊的潛在得分。

Furthermore, to see the actual benefit passing would have for teams, the barplot in Figure 12 was created to display the total expected points a team would earn if they were to pass the ball every time (of course, out of pass/run plays only) in the 2019 season. The y-axis (points added) was calculated by multiplying the number of run plays a team had in 2019 by the discrepancy between their pass and run EPA averages. It is important to note that this graph can be run independently of teams’ previous plays in a game since it was concluded above that both the values of passing and rushing are not at all affected by the proportion of prior plays being the same or opposite play.

此外，要查看傳球給球隊帶來的實際收益，創建了圖12中的條形圖，以顯示如果他們每次傳球都可以賺取的球隊的預期總積分(當然，只有傳球/傳球無效) )在2019賽季中。 y軸(加分)是通過將團隊在2019年的跑步次數乘以他們的傳球與EPA平均值之間的差異來計算的。 重要的是要注意，該圖表可以獨立于球隊先前的比賽來運行，因為上面得出的結論是，傳球和沖球的值完全不受先前比賽相同或相反比賽的比例的影響。

On average, teams would have produced 34.33 additional points in their 2019 season had they passed the ball every time. The San Francisco 49ers’ potential 113 more points are astonishing, yet not surprising. The Niners were one of two teams that were running more than they passed, yet with a stellar QB in Jimmy Garoppolo, they averaged nearly twice as many PYPA (8.4) than YPC (4.6) and had the fifth-highest average pass EPA n. Why the 49ers decided to run the ball more is inexplicable. What is surprising, however, is that more successful teams would have benefitted even more from calling more pass plays. Six of the eight teams that made the division round of the playoffs and three of the four teams that made the conference championships were above the mean potential points added in Figure 12.

平均而言，如果他們每次都傳球的話，球隊在2019賽季會多得34.33分。 舊金山49人隊的潛在113分之多令人驚訝，但這并不奇怪。 Niners是兩支球隊中跑分超過他們的球隊之一，但吉米·加洛波洛(Jimmy Garoppolo)的QB表現出色，他們的PYPA(8.4)平均得分是YPC(4.6)的兩倍 ，并且平均得分EPA n排名第五。 為什么49人決定更多地控球是無法解釋的。 然而，令人驚訝的是，更多成功的球隊會從更多的傳球中受益。 進入季后賽分區賽的八支球隊中有六支，參加會議冠軍的四支球隊中的三支超過了圖12中添加的平均潛在點。

Conclusion

結論

Teams have the opportunity to score more points by only passing the ball more often. Also, better teams tend to have greater margins between their average pass and run EPAs (take a look at the margins of these successful teams in Figure 11: Ravens, 49ers, Cowboys, Chiefs, Saints, and Seahawks). Despite these facts, teams are unwilling to experiment with their pass/run ratio — after all, teams cannot run statistics experiments when their seasons are at stake. However, what can be confirmed is that fans will continue to see teams pass more and more in the coming years. Josh Hermsmeyer of FiveThirtyEight puts it best: “The NFL is a passing league that somehow doesn’t pass enough. NFL teams know the medicine works yet stubbornly refuse to take a clinically effective dose.”

球隊只有通過更頻繁地傳球才有機會得分。 而且，更好的球隊往往在平均傳球和EPA交易之間有更大的利潤空間(請參見圖11中這些成功球隊的利潤：烏鴉，49人，牛仔，酋長，圣徒和海鷹)。 盡管有這些事實，但車隊不愿意嘗試通過率/奔跑率—畢竟，當賽季處于危險狀態時，車隊無法進行統計實驗。 但是，可以確定的是，在未來幾年中，球迷們將繼續看到越來越多的球隊通過。 FiveThirtyEight的Josh Hermsmeyer說得最好：“ NFL是一個傳球聯盟，以某種方式還不夠傳球。 NFL小組知道這種藥有效，但頑固地拒絕服用臨床有效劑量。”

Credits

學分

Andrew Cramer, Atharv Karanjkar, and Ethan Schwimmer were also members of the initial project and instrumental in generating ideas, coding, and modeling.

Andrew Cramer，Atharv Karanjkar和Ethan Schwimmer也是最初項目的成員，并在產生想法，編碼和建模方面發揮了作用。

Sources Used

資料來源

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https://www.sharpfootballstats.com/situational-run-pass-ratios--off-.html

https://codeandfootball.wordpress.com/2013/10/11/the-very-murky-world-of-offensive-srs-and-defensive-srs/

https://codeandfootball.wordpress.com/2013/10/11/the-very-murky-world-of-offensive-srs-and-defensive-srs/

https://www.footballoutsiders.com/stats/nfl/offensive-line/2019

https://www.footballoutsiders.com/stats/nfl/offensive-line/2019

http://www.footballperspective.com/why-do-teams-run-the-ball-part-iii/

http://www.footballperspective.com/why-do-teams-run-the-ball-part-iii/

https://www.espn.com/nfl/story/_/id/8379024/nfl-explaining-expected-points-metric

https://www.espn.com/nfl/story/_/id/8379024/nfl-explaining-expected-points-metric