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  • Comparing Hurts vs. Mahomes and What Has Changed Since 2022

    Comparing Hurts vs. Mahomes and What Has Changed Since 2022

    As a Penn Stater (and yes, an Eagles fan) it makes me very happy to see Saquon Barkley getting a chance at a Super Bowl ring after suffering with the Giants for a few seasons. From the initial reaction I saw, not everyone was pleased with a Chiefs/Eagles game again. I had a small preference for the Bills over the Chiefs, but now they get a chance to beat the best. Even though this is a rematch of the Super Bowl two years ago, there are still a number of things that have changed since the last time these two played. To prepare for the game I spent some time playing with the publicly available play by play data to see what interesting things we can find out about this matchup.


    Using data from the nfldatapy Python library, I primarily focused on the quarterback matchup we will see this week. To start, I was curious how the play calling distribution has changed since the last time these two teams played in the Super Bowl. Since that time, the Eagles have acquired Barkley (and lost Miles Sanders) and have a new offensive coordinator in Kellen Moore. For the Chiefs, Kareem Hunt is now the most used running back (Pacheco led the team in rushing attempts in 2022) and they have a new offensive coordinator since the last Eagles/Chiefs Super Bowl — but Matt Nagy has been around for a few years now in that role with the Chiefs, so it’s not his first season (or Super Bowl) with the Chiefs.

    In 2022, the Eagles averaged 31 pass attempts/game and 32 rushes/game per pro-football-reference. In 2024, that has shifted to 26 pass attempts/game and 36 rushes/game. (For this article I used all games to keep things simple, but acknowledge that Hurts only played in 15 games missing a few with an injury, and Mahomes also sat out the last game of the regular season.) The Chiefs were closer to their 2022 distribution this season, averaging 38 pass attempts/game in 2022 (vs. 35 pass attempts/game this season) and 24 rushes/game (vs. 26 rushes/game).


    Adding Barkley only increased the rush attempts/game by +4. But those extra 4 rushes/game (a 12.5% increase over 2022) resulted in a 21% increase in total rushing yards in 17 games (with 2022 including the Super Bowl in those stats). The Eagles’ success rate on rushing plays (defined as gaining at least 40% of the yards required on first down, 60% on second down, and 100% on third and fourth downs) decreased from 56.3% in 2022 to 47.7% in 2024. Of course, rushing stats are going to be a combination of play calling, running back performance, and offensive line performance, so there are a few factors at play here with these evaluations. I was surprised to see the success rate lower with Barkley than with the 2022 Eagles, but some of this could be related to the attention Barkley attracts. His explosiveness might cause defenses to try to stop the run more, making the average play less successful but still having a rushing attack that produces more yards overall due to enough big plays — Barkley has had 21 runs that went 20+ yards this season (5.1% of his attempts) vs. 9 runs for Sanders of that length in 2022 (3.0%).

    Looking at the passing matchup, Hurts is averaging 6.1 air yards per completed pass vs. 4.5 for Mahomes. (A quick search for Air Yards rankings reveals various numbers for season long averages — my method here is using nfldatapy to remove two point conversion attempts and sacks when calculating averages and otherwise trusting their air yards column.) On incomplete passes Hurts is averaging 11.0 air yards/attempt vs. 10.5 air yards/attempt for Mahomes. Not surprisingly, average attempted air yards is higher for incomplete passes than completed passes, but I was surprised Hurts had a higher average air yards in both categories (I don’t watch all Chiefs games, so this is just based on what I have seen).


    The next item I looked at were the favorite targets of both players. The following table shows the top five most targeted players by each quarterback, with the percentage of targets shown:


    Brown and Smith lead the way for the Eagles with 24.5% and 22.0% of targets, respectively. For the Chiefs, Kelce is the most targeted (24.1% of targets) followed by Worthy at 18.3%. The top five targets for Hurts account for 80.8% of all targets, while the top five for Mahomes only account for 67.9% of targets. Although I didn’t look into it for this article, comparing the distribution of pass targets to team success or other metrics would be interesting to see if there are any trends there (does a more even distribution of who you throw the ball to really impact any metrics?).


    What I really wanted to create was a map of targeted locations on the field for each quarterback (like I did for a Penn State/Iowa game in 2020), but that data is not publicly available to download yourself for NFL and I certainly didn’t chart every game, though you can play with these on the Next Gen Stats website.

    That said, nfldatapy does provide information on pass location broken out by left/middle/right side of the field. We can then combine this with air yards to get an idea of where players are passing the ball most, even if the exact location is not known. First I looked at the distribution of throws to side of field (left/middle/right) by complete vs. incomplete passes. The following table shows the distribution of passes that were thrown to each part of the field by quarterback separated by compete vs. incomplete attempts.


    The first thing that stuck out to me was the consistency of Mahomes in the distribution of throws to each side of the field when comparing complete vs. incomplete passes. All three distributions were within 0.5% of one another when comparing the completed pass sample to the incomplete pass sample. This is in contrast to Hurts, where 36.9% of his completed passes are to the right side of the field but 48.4% of his incomplete passes are to the right side of the field. Put another way, knowing whether a pass was completed or not would not be a helpful predictor in determining which side of the field Mahomes threw the ball to, but it would be helpful for predicting which side of the field Hurst threw it to. If it was a complete pass, we would guess it was most likely he threw to the left side of the the field.


    Combining our air yards and the field location data we do have access to, we can start to build a picture of where each quarterback throws the ball (regardless of whether the pass was completed or not). The following tables show the distribution of pass targets by air yards and location of the field:


    Nearly one out of every four pass attempts by Mahomes has negative air yards (23.2% of targets) vs. 14.5% for Hurts. The majority of both players pass attempts are in the 0-9 air yards category with 55.9% of Hurts passes falling in that category and 50.3% for Mahomes. Hurts had a slight preference for the left side of the field in that category (26.3% of all throws) and Mahomes to the right (21.3% of all throws). I would guess that a lot of the differences in side of field are down to scheme and where favorite targets line up/have routes going, but the data I am using does not have route information so it’s not easy to confirm that. Using FTN Data via nflverse we can find out that 13.6% of non-sack pass plays were screen plays for Mahomes vs. 8.3% for Hurts, which explains some of the negative air yard trend.


    The final analysis I did was looking at completion percentage vs. air yards of a pass attempt. Simply, does either quarterback have a higher completion percentage when throwing the ball a certain number of air yards? To answer this, I fit a simple regression model with air yards as the predictor and whether a pass was completed or not as the outcome variable. I chose a cubic model to capture some changes in accuracy by distance while offering up the ability to have more than one inflection point, if accuracy had a second shift at any point. (This is admittedly a simple model. Another option would be to group air yards into buckets and just report the average completion percentage, but I wanted a model that could show the probability of a completed pass for any individual attempt and the general trend.)


    The following chart shows the probability of a completed pass (y-axis) vs. air yards (x-axis). The model trend line is plotted and individual pass attempts are shown as points.


    Two items stood out to me. First, the trend lines track very closely from 0-25 air yards. Hurts has a slightly better predicted completion percentage in the 0-10 range and then Mahomes is slightly above in the 10-25 range, but they are relatively close. The second thing that jumps out is the divergence at around 25 air yards. After 30 air yards, Hurts completes around 43% of his passes and Mahomes completes around 20%. So why does the Hurts trend line jump up to 85% predicted completion percentage at 45 air yards? The primary reason (and the reason we wouldn’t want to use this model as is for predictions into the future) is that Hurts has far less pass attempts at that range and we are over fitting to the little data that is there.


    Although the previous table showed that both quarterbacks threw around 4% of their passes 30+ air yards, Mahomes has more pass attempts this season overall. Hurts has 16 passes of 30+ air yards to Mahomes’ 25, and once we filter for just 40+ air yard passes, we get just two samples for Hurts and 11 for Mahomes. Hurts completed 1/2 of 40+ air yard passes, while Mahomes completed only 22%. Some of this could come down to when each quarterback decides to throw the ball deep as well. If Hurts is more choosy in when he throws downfield, he might convert at a higher rate, but I wouldn’t expect that completion percentage to be 85% on balls thrown 45 yards in the air once we got a significant number of samples. So while we wouldn’t have a ton of confidence in this model to predict future completion percentages due to the small sample size, it does reveal some interesting trends about observed completion percentage this season. If/when Hurts does throw it deep, we might expect those situations to occur when receivers are more clearly open. If the splits of pass/run play choice from the regular season continue, we would expect Mahomes and Hurts to have an equal percentage of their passes down field, but Mahomes would have more overall chances, and we’d expect a lower completion percentage on those balls for him than Hurts based on our simple model.


    While this is really just the surface of things we could look into ahead of the game, I think the quarterback comparison is always interesting, particularly with run/pass tendencies and targets. Even though these two teams played just a few seasons ago in the Super Bowl, with the Eagles shifting more towards a run dominant team, the offensive matchup will look a bit different this time.

  • Mahomes Isn’t The Only One Flag-Baiting

    Mahomes Isn’t The Only One Flag-Baiting

    There was a lot of noise this weekend—and rightly so—about Patrick Mahomes baiting defenders into late hits in hopes of drawing flags. He was able to get one early on in the game, but didn’t fool the refs later on when he loitered on the edge of the boundary and then flopped out of bounds once defenders arrived.

    For a lot of fans, I think the frustration came fast because it was reminiscent of another moment earlier in the season when Mahomes drew a foul on another (questionably) late hit.

    This was in the 3rd quarter:

    And then the no-call in the 4th quarter:

    This was a similar play that went viral earlier in the season where Mahomes danced near the sidelines and turned it into a big gain:

    To be fair to Mahomes, there were many more examples (just search “Patrick Mahomes late hit” on your favorite social media platform) throughout the season where flags were not thrown for hits near the sideline.

    Interestingly, if you search the same terms with “Josh Allen”—probably the second most popular quarterback in the NFL—it returns a lot less results. So it does seem like Mahomes is a bit of an anomaly due to his outsized celebrity (maybe from all the State Farm commercials).

    But I did want to try to find out if there was truly some bias towards Mahomes and the Kansas City Chiefs in general when it comes to these late hit fouls. So I took a look at the play-by-play data from nflfastR and FTN Data via nflverse, which charts certain movement data like whether a QB went outside the pocket.

    Before we begin, there are two terms to define here. A QB scramble is defined by nflverse and is always a run play. It’s basically anytime a pass turns into a QB run. The QB being out of the pocket is anytime the QB exits the pocket.

    A QB scramble almost always means the QB is out of the pocket, but a QB being out of the pocket does not imply a QB scramble. In fact, a QB being out-of-the-pocket is a pass 68% of the time to a run 24% of the time, and is a QB scramble 25% of the time.

    The base dataset is all plays where a penalty was called on the defense that were not interceptions or fumbles lost.

    First off, I looked at unnecessary roughness and roughing the passer penalties specifically since these are the two penalties commonly called for late or illegal hits. According to the NFL rulebook, roughing the passer can be called inside or outside the pocket. If a QB is outside the pocket and on the move, they are allowed to be hit low or high (unlike when they are in the pocket), however if they stop moving and return to a “passing posture”, they can no longer be hit low or high again.

    The findings were interesting. Looking at plays where the QB was out of the pocket, Buffalo actually led the way with five unnecessary roughness or roughing the passer calls on the defense. KC only had one recorded.

    Unsurprisingly, some of the teams leading the way in this area have very dynamic, mobile quarterbacks: Josh Allen, Kyler Murray, Jayden Daniels, Justin Fields/Russell Wilson. However, the numbers are pretty low overall so far.

    Of course, the data is not perfect. There may be calls missed and charting data on in vs. out of the pocket is not always reliable either, plus it’s not guaranteed that the unnecessary roughness calls were called for hits on the quarterback. But we can still learn a lot from the general trends.

    Let’s cut the data a few more ways. First, let’s normalize for how many out-of-pocket plays a team has to see who’s getting the most calls proportional to the number of rollouts they do.

    All the below plots will be looking at the roughing the passer and unnecessary roughness calls only.

    Buffalo still leads the way on penalty calls when the QB is out of the pocket on a per-play basis, averaging about .3 penalty yards per out-of-pocket play, or about a call every 50 rollouts. As you can see, KC is near the bottom in this category.

    When we look at totals, the result is mostly the same. However, we’re only looking at one or two calls per team. Note that it is possible for the penalty to be less than 15 yards in both cases because they can be called within 15 yards of the goal line.

    Next, let’s look at QB scrambles where the QB becomes a runner. At this point, we shouldn’t see any roughing the passer calls but will still see the unnecessary roughness calls for late hits. There’s a lot less data here; there were only 10 unnecessary roughness calls out of 1181 QB scramble plays.

    Minnesota and Arizona both drew two of these penalties while the other teams had one. KC is notably not on this list (although please fact check me here because, as I said, the data may not be perfect. I was able to find two instances but they occurred on turnovers.)

    Last, let’s take off all the reigns and look at these two penalties across all plays, including those where the QB stays in the pocket.

    Here, Miami creep up as a big beneficiary of these calls (is Tua Tagovailoa’s injury history playing a factor here, perhaps?). Again, the Chiefs are very middle of the road here, showing no evidence of favoring Mahomes.

    On a total yardage view, there isn’t much change.

    Just for fun, let’s see who’s getting the most flags called against their opposing defense overall, without any filters on turnovers or penalty types.

    Here, we see that Minnesota, Washington, Dallas, and Buffalo drew the most penalties while Jacksonville, Indianapolis, and Detroit drew the least.

    While this is interesting, nothing really stands out and the distribution of penalties across the teams is fairly normal.

    So, to summarize, I think that fans have a short memory and also a memory that highlights the moments and players that are most infuriating and impactful to the result of the game. Patrick Mahomes is a world-class player and he’s on TV all the time. He’s also had some moments that have been A) on national television or B) gone viral where he has seemingly flopped or earned a call from the refs that was undeserved.

    That being said, I found no evidence in any of the data that the Chiefs get any more calls than other teams. In fact, they rank near the middle or bottom of most of the charts above.

    I agree that the NFL does need to figure out how to regulate the “QB as a runner” situation a bit better, because it is hugely disadvantageous to the defense when quarterbacks get these special protections near the boundaries that other players seem not to get. Whether it’s actually the case that these calls are made more often on quarterback runners than other runners is a whole other investigation (a quick glance gives me the impression that this is not actually the case).

  • Norris Qatar Grand Prix Throttle Trace Analysis

    Norris Qatar Grand Prix Throttle Trace Analysis

    At the Qatar Grand Prix on Sunday, an errant side mirror from the Williams ended up on the front straight, causing some brief yellow flags that Norris did not heed to, resulting in a 10-second stop-and-go penalty and effectively ending his day.

    Let’s take a look at the data and see what actually happened.

    A wing mirror sits in the middle of the straight as Perez and Hamilton drive by.

    First, lets look at the normal braking points for reference. This was the lap before the yellow, lap 29:

    Between 600-650 meters is the normal braking point.

    Now lets look at the next lap, focusing on the top drivers who actually saw the yellow flag—remember, the yellow flag only came out for a moment, and then was removed for several laps until the mirror was run over by Bottas.

    Here we see that the flag actually came out late into the straight for Max, who throttled down almost 200 meters before normal. Norris was further back down the straight and did not lift, and Leclerc (who was behind Piastri, not shown) lifted a quite early, throttled back up, then down again.

    Piastri was right behind Norris and lifted very early, while Sainz was behind Leclerc and throttled down about 100 meters early. And by the time Perez came around, the flags were gone.

    By these traces, it’s clear that Norris was the heaviest infringer here. So I’d say a penalty was warranted. However, a few questions remain.

    First, what’s considered sufficient slowing for a yellow flag? Verstappen lifted completely, while Leclerc lifted slightly and seemed unsure of where to continue on, and Sainz went through the majority of the flag area at full throttle and lifted near the actual debris.

    And second, why was the yellow flag rescinded with no change in the situation? If the mirror was deemed hazardous, then every driver should have been made to lift in that section (or more reasonably, a VSC should have been deployed to keep the field even until the piece was removed).

    This issue was clear as day and folks at home could have reacted in an instant, but it took the race director six laps to decide what to do, and it was only prompted by Bottas hitting the debris and two punctures coming at the same time.

    Just to emphasize the differences, here’s the speed differences of the Top 6 going into turn 1 on Lap 30, and as you can see Norris is just well ahead of the rest of the field (although I should note that the rest of the field, with maybe Max as the exception, were all going almost 300 kph through the majority of the straight until the actual debris point.)