Cape League Catcher Reports

Measuring and developing a catcher's performance using Trackman Data.

Catcher defense has always been my favorite part of baseball analytics, and so I knew right away that I was going to work on a catcher project with the Harbor Hawks. It ended up being my proudest work of the summer, and I hope to consistently use and improve the project into the future. I’ll start with my initial goals before explaining each component of the report and how they were used. I’ll also create a post soon that details the best catchers on the Cape this season by my metrics. Feel free to comment here or on Twitter if you want to see the report from a specific Cape League catcher!

Goals

The reports need to…

  • Have a single game option in addition to season-long, in order to continually grade and improve our catchers

  • Have a comparison feature to give context for the numbers

  • Be understandable and applicable for stats nerds, catchers and coaches alike

  • Be neat and visually pleasing, easily comprehensible

  • Be multifaceted, giving insight to framing, blocking and control of the running game

  • Be repeatable, so that I can easily and automatically generate new reports every day

  • Be available in PDF form for easy distribution and use

Strike Zone Scatterplot

The first step for the report was a fairly simple scatterplot of the Strike Zone with every single called pitch from the chosen game or season. For a single game, you can look at what happened on each individual pitch. With a small sample size, though, each pitch or game could be a fluke, with the umpire heavily swaying the results. I recommended we almost always use the season-long feature, where you can start to see densely colored areas where the catcher is excelling or struggling. I also displayed a “Shadow Zone” which is the area within about a ball of the strike zone, both in and out. This scatter chart was immediately understandable to any coach or player, so I decided to keep it as the centerpiece of my report.

Strike Probability Model

With that simple scatterplot, though, I recognized that the report needed more context and nuance. If you only saw that one plot above, you might think that catcher was elite at stealing strikes off the plate, but that’s not necessarily true. Cape League umpires give about a ball on either side of the zone, consistently calling wide strikes. I approached this problem with a strike probability model, which lent itself to a Strikes Looking Above Average (SLAA) stat.

After talking with another intern Aidan Beilke, I trained the model using XGBoost with just batter handedness, pitch vertical location and pitch horizontal location as features. With more data I would want to include the umpires as a training variable, but I still achieved close to 90% accuracy. Similar to Outs Above Average, my SLAA stat was the difference between the modeled strike probability and the actual result. Summing this stat for each called pitch gives the game, or season, SLAA. Because this statistic is compared versus the predicted average Cape League catcher, it gives a lot more context than simple balls and strikes called. Additionally, I think it helps that it’s a stat where 0 is exactly average. To give a sense of scale, I also displayed a catcher’s SLAA percentile on the report.

Still, this doesn’t necessarily help a catcher improve. Simply telling them that they’re a good or bad framer likely means nothing. So I converted SLAA into a Strike Zone hexmap to give players a better idea of where they should focus on practicing. Here is the corresponding SLAA graph to the scatterplot and percentiles above.

Pitch type table

My last framing piece was splitting framing by pitch types. I figured that catchers might have difficulty with different pitches, and so they can look at their technique on those specifically. In the Cape League, I noticed that catchers consistently performed worst on off-speed pitches, at least when it came to the Shadow Zone Strike%. While that could be something for certain players to work on, the fact that it was universal is more a sign that off-speed pitches are harder to frame.

Running Game

Unfortunately, Trackman does not keep data on blocks and passed balls, so I wasn’t able to incorporate that. With a main focus on framing, the only secondary part to the report was the running game. I’m not completely confident in the Trackman pop times, but I thought at the very least the catchers could use exchange time, throw speed and these throw accuracy charts. The season long options creates averages by base for the metrics on the table.

Formatting

Even after all the modeling and visualization was done, the majority of my time on this project was spent on the PDF formatting. Like I said in the goals section, a key part of this project is its ease-of-use. I strongly believe that an unappealing or messy report won’t get used as heavily by the coaches and players. Aidan recommended I use Python package FPDF, which works out well with a lot of trial and error for positioning your elements. I separated my charts and tables by Framing and Running, and emphasized the main two elements: the pitch call scatterplot and the total SLAA number and percentile.

Single Game:

Full Season:

Report Applications

When I completed this project midway through the season, our primary catchers were Jaxson West (Florida State) and Cannon Peebles (Tennessee). Both ended the season with above average framing, and they were devoted to sharpening their craft. At different points, I showed them both their season-long framing reports, explaining what each thing meant and what I thought they would work on. Jaxson, for example, was losing strikes at the bottom of the zone, which held back his plus framing everywhere else. At this point in my career, I’m not comfortable suggesting mechanical tweaks, but in the future I look forward to combining statistical suggestions with physical ones.*

Future of Catcher Reports

For the most part, I’ll be able to easily translate the Catcher Reports over to the NCAA season considering schools use the same Trackman system as the Cape. However, I’ll have to make a decision on how I train the model– with a small 10-team league like the Cape, I felt comfortable assuming the umpires would have a small and consistent rotation. But with over 300 D1 baseball teams and almost 30 conferences, I can no longer make that assumption. The best option might be to give SLAA scores that are trained and compared only to a catcher’s own conference.

Additionally, a long term goal would be to create MLB catcher reports, which I haven’t seen any of in the sports data world. MLB StatCast would open up a new world of blocking metrics, and also allow the extra variable of umpires. Maybe someday there can even be a Twitter bot for these reports.

Thank You’s and Credits




Thanks for reading, and let me know if there’s a Cape catcher report you want to see!

*And if you have any recommendations on catcher coaching books, articles, or videos, please share them!

Cape Cod Baseball League wOBA Prediction Model

My first project from my summer with the Hyannis Harbor Hawks.

When it comes to Cape League roster attrition, there is only so much you can prepare for. In March, every team’s roster looks great, full of All-Americans and future first-rounders. On Opening Day in June, the managers are probably still pretty happy with where they stand– maybe a pitcher or two dropped out, but the core of the roster is still intact. Soon after, though, the Cape teams are scrambling. The ace of your rotation is getting shut down for the summer. An infielder hit the transfer portal and needs to take visits. A school keeps a high leverage reliever at school work on a strength program, and your top option for replacing him is already in another league. Your first baseman’s team goes on an unexpected run in Omaha, Team USA poaches a few of your very best, and then an MLB team decides to take a flyer on your starting centerfielder. This isn’t specifically what happened to the Harbor Hawks, or any Cape team, but it’s accurate in terms of quantity and impact. The reasons may be different for each player and team, but no matter what, every roster looks completely different during the August playoffs than it did in the preseason.

The replacement game is critical for Cape League teams, and our operations/analytics group for Hyannis was empowered to find potential players and suggest them to the Front Office. Hyannis used 27 batters and 32 pitchers over the course of the season, seriously considering all of our suggestions, some of whom became critical players. After a semester learning how to use machine learning models in Python, I wanted to try to use basic NCAA stats to predict Cape League offensive production. Ideally, this model would identify overlooked candidates for further vetting and also give some insight into how a batter’s conference can impact their Cape performance.

Data Preparation

My NCAA data came from baseballr (Bill Petti and Saiem Gilani), where I was able to pull stat lines for every D1 player for years 2021-2023. Unfortunately, trying to pull 2019 caused errors I couldn’t quite fix or explain, so I had to go with just three seasons. A possible next step in this model would be to instead use Robert Frey's collegebaseball package whenever he finishes it. With the current setup, I went year by year and pulled players from each university in that year. The data came with some column shifting issues, which I fixed in R and then wrote to a CSV for each year.

Conference strength ratings were a key goal of this project from the very beginning, because with apologies to Gabe Appelbaum, OPSing .900 in the Atlantic 10 is pretty different from doing it in the SEC. Boyd's World was the best resource for this, so I pulled season-end conference ratings for each year I was considering. Considering schools play fairly different non-conference schedules, a more in-depth approach would be to look at a more comprehensive strength of schedule metric.

Cape Cod League stat lines were the final peace of the puzzle, and I calculated those with the play-by-play files I had. I brought the CSV’s into Python, where I’m more comfortable and know the machine learning packages. Before merging, I chose to replace certain names for a common standard. For example, if a “Zachary” was listed as “Zach” in the NCAA data, I wanted to make sure his data correctly merged with the Cape data that listed him as “Zachary.” ChatGPT helped to generate a list of common names and their replacements. (Michael → Mike, Steven → Steve, etc.) I also stripped all whitespace to get rid of any inconsistencies in that area.

After merging the trifecta of college data, college strength of conference and Cape data, I used the 2024 MLB wOBA weights to calculate both NCAA and Cape wOBA. This was certainly not an ideal strategy, and eventually fellow interns Gabe, Aidan and Richard calculated Cape wOBA, but at the time this was the best I had. If anyone has NCAA wOBA weights, feel free to reach out, but of course the run environment will be incredibly different across divisions and even conferences.

The Cape wOBA was my target (or “Y”) variable, and the NCAA stats (including wOBA and conference rating) were my feature (or “X”) variables. After splitting my data into an 80/20 train/test split to score the models, I went into training.

Model Training

I initially went with four regression models to compare:

  • Multiple Linear

  • Bagging

  • Random Forest

  • XGBoost

Here are their scores without any tuning:

The more advanced model types are classically overfit, which can be improved by both more data and better tuning. (Side note: What other model scores should I be looking at?) The bagging model was the most promising, so I moved forward with that one.

Model Tuning

I used three strategies to tune the bagging regressor model:

  • Grid Search testing to find the best parameters for number of estimators, maximum samples to use to train each estimator and maximum number of features to use to train each estimator

    • Result: n_estimators=50, max_samples=.6, max_features=.2

  • Recursive Feature Elimination to select the optimal number and subset of features. Another intern recommended I sum doubles and triples into one column, which helped the model.

    • Result: AB, H, 2B+3B, HR, SO, BB, OBP, SLG, wOBA, Conference Rating

  • At Bat Qualifiers to have a hefty sample size for each player in both of their seasons. After starting off with a one per game qualification to use for training and testing (56 for NCAA season, 44 for Cape season) I tuned this for the best possible model.

    • Result: NCAA Season minimum 56 AB, Cape season minimum 50 AB

    • 204 Rows before train/test split

Model Accuracy Results:

While still clearly overfit, the tuning process improved sliced my error metrics in half. With the standard deviation of Cape wOBA at .0563 for these years, I felt like the model was fairly good. To put that into perspective, I found that the predicted Cape wOBA was within .09 over 92% of the time. So it’s not exactly a pinpoint predictor– if my model predicted a player’s wOBA at .320 (above average), we would be 92% sure he would actually land between a .230 and a .410 wOBA on the Cape, the difference between an abysmal season and a historically great one. I’ll get more into how I actually used the model in a later section.

It makes sense that NCAA wOBA was the most important feature, and that OBP was the next most. The early assumption that conference strength was an useful feature also turned out to be true. Our General Manager Nick Johnson always says that on the Cape, “the whiff translates, but the power doesn’t,” and now he’s got some data to back him up. Strikeouts were much more important to the model than home runs.

The fun part for me was looking at what the model actually predicted for 2024, and seeing if it passed the hype test. I took the 2024 NCAA data and used it to predict what each player’s Cape wOBA would be. Here are the top 5 predicted Cape wOBAs:

TOP 5 CAPE WOBA PREDICTIONS

This is a list that encompasses the best and worst of my model. At first glance, I was excited to see Charlie Condon and Jac Caglianone highly ranked, as the two best hitters in college this year. I tried to tell the Harbor Hawks to consider taking them, but apparently they were busy with the MLB Draft or something. The two guys above them are more interesting. To me, Mark Shellenberger’s (Evansville) prediction isn’t bad at all. He had an incredible college season with a 1.2 OPS and walked 50% more than he struck out. He’s not Condon, but I would love to have him on the Cape. My model doesn’t know about eligibility though, and he’s exhausted his. He did, however, put up some good stats in the Northwoods and Cape Leagues in previous years before slumping in the MLB Draft League this summer. According to his Twitter, continues to wait about undrafted free agent opportunities, and I would love to see a team give him a shot.

Jordan Smith (George Mason) wasn’t as impressive as Shallenberger and it was in a weaker conference, but I do at least see some of the vision. He strikes out a lot but hits for some solid gap power. He’s done with college baseball as far as I can tell, but I’m not sure where he’ll go next. Preston Shelton (Murray State) is where the model gets way off the mark– an OPS of .658 in an average conference? I just can’t grasp what the model saw in him, but maybe a few very similar profiles raked in the Cape League and this is just another problem with overfitting.

Want to look at the model’s prediction for your favorite player? See the Google Sheet here.

Applications

Due to the low confidence levels of my model and some obvious whiffs on certain players, the predictions became more of a name-finder than anything. When we needed to fill a need at a certain position, I would filter for freshmen and sophomores at that position, sort by their wOBA prediction and go down the list. Isaac Wachsmann (Xavier) was a player I found this way, who I felt like was an under-the-radar Cape candidate with a .322 wOBA prediction (73rd percentile). We needed a corner outfielder at the time, and the model liked him, so I dove deeper:

  • This year, 1.132 OPS with .700 SLG. in 108 PA

  • Got some BABIP luck but still pretty interesting.

  • 10 HR, really good for his number of PA

  • 24.1% K Rate, 11% BB rate.

  • Somewhat passive in terms of swinging: 66% IZSwing, 18% Chase, 30% FPS

  • vs 90+ mph (n=125 pitches):

    • .526 SLG

    • 14.3% Chase

    • 11.1% In Zone Whiff

    • 15.6% Whiff (fairly low whiffs)

    • 91.2 avg ExitVelo

    • 111 max ExitVelo,

    • 10% Barrel

  • vs 75+ Breakers (n=121):

    • .667 SLG

    • 32.8% Chase

    • 16.7% IZ Whiff

    • 36.2% Whiff

    • 93.8 ExitVelo

    • 111.5 Max ExitVelo

    • 22% Barrel

  • Gonna miss a lot verse breakers and doesn't have great swing deciscions overall, but I don't think it's unplayable given what we need on our team at the moment. Exit velos against those good pitches are pretty promising as well. Swing looks quick with nice hands, good line drive hitter

  • I'm going to take a deeper look at defense later, first look it seems playable but average at best as a corner OF.

The model found the name, but I did a lot more work before I put him in front of my bosses. In the end, we didn’t sign him, but Orleans actually did. He slashed .188 / .229 / .344 for them in 35 plate appearances a (low sample) miss by my model.

Lessons + Extensions

I would love to extend this project with advanced data on each hitter rather than just the traditional counting and rate stats– specifically, I think Whiff%, Chase%, Launch Angle and EV90th (or your Exit Velocity stat of choice) would bring this model even further. Plus, of course, more data is always better.

Although the model didn’t become a go-to tool for Hyannis, the process still taught me plenty. Just like you can expect to find in data science everywhere, the most frustrating and time consuming part was just the data scraping and cleaning. Tuning was tough as well, but ultimately rewarding as the model improved with each iteration. Taking this project from raw data to final predictions developed my skills in many different Python concepts.

Thanks and Credits

  • Thank you to the other analytics interns: Aidan Beilke, Gabe Appelbaum, Richard Legler and Tyler Warren. Richard recommended Boyd’s World and Tyler shared his code to convert play-by-play files to player stat lines. All four were incredibly helpful when I had questions about code and model tuning.

  • This video from Robert Frey helped a lot when it came to using the baseballr package for NCAA data. Excited to see more with the collegebaseball package!

  • Thanks a ton to the work done by Boyd's World to create conference strength ratings, not to mention the other fascinating stuff on his site. My personal favorite is the External Factor Index

  • Credit to the creators of the baseballr package, Bill Petti and Saiem Gilani.

Anthony Davis Doesn’t Deserve His Max Contract

Written By Nicholas Penaloza and Martand Bhagavatula

Coming into the 2021-22 NBA season, many believed that the Lakers were title favorites. Although they lost key role players like Alex Caruso in the offseason, they acquired 9x All-Star Russell Westbrook, seasoned veterans like Carmelo Anthony and Dwight Howard, and spark plugs like Malik Monk and Kendrick Nunn. These acquisitions fostered incredible pre-season hype for the team, ultimately materializing in very little to date as the Lakers just got eliminated from playoff contention. There are many causes for their disappointing season but the absence of Anthony Davis is arguably the most important. Although AD returned recently, he has largely been unavailable, only playing 39 games this season. While AD certainly deserves criticism for failing to stay healthy, an argument remains that he is one of the most versatile two-way players in the NBA when healthy. However, being injury prone has cast doubt on whether AD deserves his max contract (5 years, $190 million), the 15th highest player salary this season. ​​To answer this question, we utilized the lenses of availability and production, creating three graphs to support our argument.

  1. AD’s salary over his ten-year career versus his availability, measured in the % of the season played

  2. Comparing AD’s advanced player metrics with Kawhi, another injury-prone superstar

  3. Comparing AD’s advanced player metrics with other superstars

     AD’s Injury History 

Throughout his 10-year career, Davis has missed approximately 23% of his regular season games due to a variety of injuries, illnesses, and soreness. His serious injuries include a stress reaction to his ankle, a fractured hand, back spasms, various knee/shoulder injuries, groin strains, MCL injuries, and multiple calf/Achilles injuries. Amongst the list of ailments, several of them have been nagging – most notably his calf/Achilles injuries that kept him out for clumps of the 2020-2021 campaign. Indeed, calf injuries are statistically very likely to recur and nag, specifically due to the constant pressure put on the injured area in every facet of the sport, as well as it being the lynchpin to leg mobility. When it comes to Achilles injuries, there’s arguably no isolated body part more important to a player’s function. It’s the primary driver of stability, explosive motion, and all (especially rapid) directional changes. The tendon takes on stress equal to over 10x your body weight, requiring that it be at full health for peak basketball performance. 


While these injuries have been dispersed across his career, the past two seasons have essentially been highlighted by Davis missing extensive time. This also coincides with when he received a max contract, meaning that for the purposes of this article, the past two seasons will be isolated. In evaluating the said time period, Davis has played a mere 49% of the team’s games (including the playoffs), most notably missing key stretches of the 2020-2021 regular season, in which the Lakers finished as the 7th seed in the Western Conference, and the tail end of the First Round of the 2021 NBA Playoffs, where the undermanned Lakers squandered a 2-1 series lead to eventual Western Conference Champions Phoenix Suns.

The current season has been no different than last, as Davis has been kept out for nearly 20 games due to an MCL sprain he suffered in December 2021 and 19 games for a severe ankle sprain in February. Thus, the past two seasons have been heavily asterisked by Davis missing extensive time, with no guarantee that this trend will end as he plays out his contract. 

The graph below illustrates this point, showing AD’s salary over his 10-year career alongside the percentage of games (regular season + playoffs) he played in each season. The biggest takeaway from the graph is that the years when AD was on his max are also his least “productive” years, from a games played standpoint. 

How Does Davis Fare Relative to Other Max-Contract Players?

Comparing AD to other max players, he is near the top of the list when it comes to total money made while injured, and time missed relative to money made. In regards to the former, Davis is in the top 3, trailing only Kawhi Leonard and Jamal Murray. While Leonard has a reputation for missing significant time, similar to Davis, Murray suffered a torn ACL in the 2020-2021 campaign without having a significant injury history, implying that his severe injury is likely an outlier, and dubbing him “injury-prone” would be an unfair assessment. Thus, the safest person to compare Davis to, in the context of being “injury-prone”, would be Kawhi Leonard. 

Using the advanced metrics of TS Add, VORP, and BPM (see glossary for explanations), Leonard trumps Davis from a production standpoint. For example, last season, utilizing TS Add, Davis presents a score of -22.7 points, demonstrating a significant dropoff in his production and presenting him as a below-average offensive player. Kawhi, on the other hand, possessed a TS Add of 105.2, indicating his superior production relative to the average NBA player (an expectation of someone playing on a maximum contract), as well as superior production relative to Davis. While Davis has returned to having a positive TS Add (20.5) this season, that number indicates him only being a slightly above average offensive player, and more importantly, pales in comparison to his own ceiling of TS Add. In the 2017-18 season, Davis yielded one of the highest TS Add’s (191.2) in NBA history.

Using VORP and BPM, which are other good representations of player value and replaceability, Davis also demonstrates considerable drop-offs, as well as relative inferiority to Leonard. 


When comparing AD and Kawhi’s VORP to other superstars, AD’s subpar production becomes even more evident, especially in the 2020-21 season. 


Overall, when it comes to max contract players, Davis and Leonard are in leagues of their own when it comes to being dubbed as “injury-prone”. From there, the only valid argument to warrant a player receiving a max contract despite being unavailable for significant time is that in the time that they do play, they perform at an irreplaceable level. For Davis though, that cannot be said. While statistically, he remains a narrowly above-average player, his unavailability paired with poor advanced metrics compared to other max-contract players constructs a very strong argument in favor of him not being worthy of a max contract. In a world where Davis was performing at the same level that he was prior to receiving the maximum contract, even with his existent injury issues, an argument can be made that due to his superiority over the rest of the NBA, he deserves the max contract. However, accounting for the production drop-off paired with nearly league-leading unavailability, there’s very little argument to be made in favor of Davis, leading to the ultimate conclusion that he is not worth the maximum contract he was given in the 2020 offseason.

Glossary of Terms 

TS (True Shooting) Added

How many points a player scored above/below what a league-average player would’ve scored given an equal number of true shot attempts.

VORP (Value Over Replacement Player) 

A box score estimate of the points per 100 TEAM possessions that a player contributed above a replacement-level (-2.0) player, translated to an average team and prorated to an 82-game season.

BPM (Box Plus-Minus)

A box score estimate of the points per 100 possessions a player contributed above a league-average player, translated to an average team.









The Wild, Wild West: Who Will Win the AFC West Title Race?

WRITTEN BY Benjamin Harris-Myers

Class of 2025, harrismy@usc.edu

This offseason has been arguably the most thrilling and impactful one in the history of the NFL. We’ve seen franchise quarterbacks leave teams they’ve been a part of for decades, last second switch ups to the detriment of some teams and jubilation of others, and record setting contracts. 

One particularly fascinating storyline has been that of the AFC west. Last year, the AFC west was one of the more competitive divisions, sporting two playoff berths and lots of compelling games (see Raiders vs Chargers week 17, Chargers vs Chiefs week 15, and Chiefs vs Chargers week 3). In the end, Patrick Mahomes and the Chiefs still won the division, but this season may not be as easy. This year, I dare say, the AFC west will be the greatest division in football history. Every team other than the Chiefs made significant additions and have seemingly become teams to beat not just within the division, but their conference. Unfortunately, just one of these teams can win the AFC west, but who will it be? 

Let’s start with the Kansas City Chiefs. Despite their bumpy start, the Chiefs made a great playoff push, losing to the Cincinnati Bengals in the AFC championship game. The Chiefs did not have the greatest offseason, losing one of the best receivers in the league, Tyreek Hill, and likely will experience a decline. However, since they were so dominant last year, it’s still unlikely that they will fall out of the running for the AFC west this year.

Like I said, their biggest loss was that of Tyreek Hill. Hill is commonly known as the fastest wide receiver in the league, and exceptional before the snap. The Chiefs got a treasure trove of picks for the receiver, but the move doesn’t make much sense to me since the Chiefs are in “win now” mode. The one argument for this trade is that they didn’t have enough cap space to restructure his deal, and might as well look for a receiver in the draft or rebuild some of their lackluster defense. 

However, as a backup to potentially missing on a wideout in the draft, the Chiefs picked up JuJu Smith-Schuster from the Steelers. Last year he played only 5 games for Pittsburgh, struggling with a shoulder injury the majority of the season. He understandably put up his worst statistical season last year, but even before that he was on the decline.



In 2018 he had his best and only Pro Bowl season, catching over 1400 yards and hauling in 7 touchdowns as a second option behind a prime Antonio Brown. I believe a lot of his success that year (and lack of success other years) was because of the attention Brown requires from the defense. Keep in mind that in 2019 Antonio Brown left the Steelers, so JuJu had the brunt of the load to carry. JuJu will be joining arguably the best tight end: Travis Kelce. This means that he will be the second option for Mahomes, taking a lot of the pressure off his back. Even though JuJu may not get the same volume of throws as he did as a one, or even a two, he should still provide much needed depth to an otherwise top heavy Chiefs receiving core. Additionally, this should take some of the load off of Kelce’s shoulders, especially with Hill leaving. Last year, the two contributed to 2475 of Mahomes’ 4839 passing yards, and with half of that production gone, the Chiefs were in need of a veteran receiver.


Another loss the Chiefs had during the offseason was Demarcus Robsinson, Mahomes’ 5th favorite target last season. Potentially to take that spot is Marquez Valdes Scantling from the Packers. He is a deep threat similarly to Hill and Robinson, but doesn’t have as much speed as he’s significantly taller. Overall, MVS is an improvement over Robinson, but the Chiefs are downgrading at receiver overall and should be less dynamic offensively next season.

On the defensive side of the ball, things aren’t as positive. During the Mahomes era, the offense has largely carried the team, with the defense just needing to be average to win the game. One of the defense’s brightest spots though was free safety Tyrann Mathieu, the Honey Badger. Honey Badger is a certified ball hawk, snatching the 5th most interceptions since joining the Chiefs in 2019. Additionally, by all accounts Mathieu is one of the big leaders on the defensive side of the ball. As of the writing of this article, Matheiu is a free agent and it doesn’t seem like he’s going to resign with the Chiefs. His impact on and off the field will be missed, but the Chiefs already have his replacement: Justin Reid. Reid is definitely one of the better free safeties in the league, but his being on lackluster Texans teams over the past couple of years has not brought him any buzz.


Even though Reid has better overall raw stats than Mathieu (Reid has fewer yards and completion percentage against him), advanced analytics and PFF suggest that the Chiefs are downgrading slightly. 

With all this being said, it’s difficult for me to say that the Chiefs will still be on top this coming season after losing one of the best wide receivers in the league, but if any quarterback-coach combo can remedy this, it’s Patrick Mahomes and Andy Reid. It’s absolutely fair that they will stay in competition with the rest of the AFC west for the division crown, but as of now it’s unclear if they will continue to be the Super Bowl contenders that they’ve been over the past 3 years.






The Las Vegas Raiders made the playoffs last year after beating the Chargers in the final game of the year. Even though they lost their first game in the playoffs, last year was a huge success for the Raiders considering what they experienced in the beginning of the season. Last year’s run  has attracted many big name players this offseason, namely Davante Adams.

On March 17th, the Raiders traded a first and second round pick to the Green Bay Packers. This is a huge price tag for a wide receiver, but it makes sense for what the Raiders are getting in return. Last year, Adams made it to first team All Pro for the second year in a row, and his 5th consecutive pro bowl appearance. It is up for debate on whether or not he is the best wide receiver in the league with players like Cooper Kupp or even Tyreek Hill, but it is clear he is in the upper echelon of wide receiver talent. He should instantly make an already impressive Raiders offense even more unstoppable.


Last year the Raiders heavily relied on Hunter Renfrow, one of the best slot receivers in the league, and Darren Waller, a top 5 tight end. The production of those two will understandably fall because of this addition, but similarly to JuJu’s situation in Kansas City, they should have many more open looks because of the threat Adams poses to any defense. As if it couldn’t get any sweeter, Derek Carr and Davante Adams played together in college and are great friends off the field. In their final seasons at Fresno State, they both lit up the field, with Carr passing for over 5000 yards and Adams contributing an absurd 1700 to his total. They haven’t played together for almost a decade, but there’s no reason to think that their chemistry shouldn’t get right back to where it was in a hurry. 

On the defensive side of the ball, the Raiders made more huge splashes with the acquisition of defensive end Chandler Jones and cornerback Rock Ya-Sin. Last year, the raiders defense in all categories was in the middle of the pack, but these additions look to make it much more impressive. The pass rush in particular should become a QB's worst nightmare. Already on the defensive line was Maxx Crosby, a first time pro bowler and second team All Pro edge rusher. He had 7 sacks last season, one fifth of the Raider’s total of 35, and Jones contributed 10 to the Arizona Cardinals’ 41 sacks. To hold down the secondary, the Raiders traded for Rock Ya-Sin from the Indianapolis Colts. The Raiders are losing defensive end Yannick Ngakoue in this trade, but Jones is still an upgrade, and Ya-Sin fills a position of need.


So where do the Raiders stand after all these moves? One of their bigger issues last year was penalties against them, specifically offensive holding and defensive pass interference.

There was little change made to the offensive line, so for offensive holding we’ll have to wait and see if they can fix their mistakes. Defensively, Ya-Sin may reduce the number of pass interferences, as he was only called for 3 penalties all of last year. On the other hand, he is just one player and it’s difficult to say that his presence alone will stop other Raiders from committing penalties. Overall, the Raiders will definitely be a better team, potentially representing the most explosive offense in the league. Just like the Chiefs, I see them as Super Bowl contenders, but it’s going to be hard to make it out of this division.

In 3rd place last season were the Los Angeles Chargers. They just missed the playoffs after losing their week 17 matchup to the Raiders. There were a lot of unknowns coming into the season, having just fired their coach, but also managing to sign center Corey Linsley from the Packers to bolster an otherwise terrible offensive line. Overall, the Chargers’ season was pretty successful, but they and Justin Herbert are looking to make a huge step forward this year. Like the Raiders, the Chargers made tons of moves all around the ball, but we’ll have to wait and see what pans out.

Arguably the biggest name the Chargers acquired was trading for Khalil Mack from the Chicago Bears. While the former defensive player of the year is coming off his least productive season to date, he should still provide huge value for the Chargers. He only played 7 games because of a season ending foot injury, but still got 6 sacks for the season. Even though he probably won’t maintain that pace for a whole 17 game slate, the Chargers don’t really need him to. Also starring on that Chargers defensive front is Joey Bosa who had 10.5 sacks for himself. Even though the Chargers were in the middle of the back for sacks last year, I see them as improving a substantial amount this year. Having two great pass rushers on the same line is significantly better than one (unless you’re Aaron Donald and only need yourself) as seen with the 49ers last season. In 2020 the 49ers lost Nick Bosa to a season ending injury, and had a lackluster season when it came to pass rush, despite having Arik Armstead who had 10 sacks in 2019 (he only had 3.5 sacks in 2020). However, when Bosa came back this year, they were 5th in the NFL in sacks with 48. Bosa had 15.5 and Armstead had 6. As you can see, a pattern of great pass rushes in this division, and I think it’s essentially a wash if you're comparing all of them. If anything, the offensive lines are going to be the difference, and as it stands only the Chiefs have a great one. 

Another huge move by the Chargers was picking up free agent cornerback J.C. Jackson. Last year the Chargers had a very solid pass defense, but ended up slim at corner because of injuries at the position, so Jackson would be filling a position of need. Even though all of their important corners were reactivated during the offseason, Jackson would be an undisputed upgrade over any of them.


The former Patriot has the most interceptions since he joined the league in 2018 (25), and made it to his first pro bowl with 8 interceptions coming last year alone. He also had a 49.1 completion percentage when targeted, which is incredible considering he’d be the one to guard the opponent’s best player every game. Jackson is also joining Derwin James, one of the best safeties in the game right now. Overall, J.C. should improve an already above average pass defense for the Chargers, which will likely prove dividends considering the explosiveness of the offenses in this division.

The final acquisition worth noting for the Chargers was tight end Gerald Everett. I’m not saying that Gerald Everett will be the one to bring the team to the promised land, but his addition is important considering the inconsistency at the position for the Chargers in general. 

Overall I believe the Chargers are another Super Bowl contender and all of their important acquisitions will be seamless fits within weeks. Just like the Raiders, the Chargers were penalized a ridiculous amount last year, and I think that is their most glaring weakness to be solved. I also don’t think their offense is as dangerous as the Chiefs or Raiders, but they will still be a big threat come playoff time with their exceptional pass rush and defense.










And finally there are the Denver Broncos. They rounded out the AFC west last year with a measly 7-10 record. They showed flashes of potential with dominant wins over the Chargers and Cowboys last year, but were ultimately limited by inconsistency at the quarterback position. To solve this issue, the Broncos made the biggest trade of the offseason, acquiring the Super Bowl winning quarterback Russell Wilson from the Seahawks.

Any team with Russell Wilson is instantly in the playoff conversation because of how generationally great he is. Last year’s Seahawks underperformed greatly but that was mostly due to how abhorrent their defense and offensive line were. According to ESPN and PFF they were ranked 28th and 25th respectively. Wilson, on the other hand, managed to make it to his 5th straight pro bowl with over 3000 yards and 25 touchdowns. These numbers are actually ticks down from his career averages, but that can be explained by the fact that he missed 3 games due to a finger injury. He came back by the end of the season, and it really shouldn’t be a concern for the Broncos coming into this year. If the Broncos have anything to worry about regarding Wilson, it should be what he’s joining on the offensive side of the ball. The Broncos offensive line, while not as bad as the Seahawks, wasn’t elite, and their weapons are young and unproven. They very well may be unproven because in the last 3 years, they’ve had 7 starting quarterbacks, and just need the reliability that Wilson had provided his whole career. Jerry Jeudy and Courtland Sutton are both wide receivers that fit this mold, but there is also concern in their own injury histories. Sutton tore his ACL in 2020, and Jeudy missed 7 games last year with a high ankle sprain after a promising rookie season. All of my predictions for the previous teams were under the impression that they would be at their full potential, so I don’t believe it would be fair to rule out the Broncos’ weapons because of their past. 

While the offensive side of the ball has many unknowns, the defensive side should be absolutely dominant. Last year they ranked in the top 10 in total yards, passing yards, and points allowed.


Rookie corner Patrick Surtain II had an incredible season, highlighted by 4 interceptions and a 51% completion percentage when targeted. Also anchoring the pass defense is free safety Justin Simmons, who had 5 interceptions of his own. The secondary didn’t see much change in the offseason, but it didn’t really need to. Their front seven, however, is a different story. They ranked in the middle of the pack in yards allowed and sacks (15th and 18th respectively) for the whole year, and don’t have Vonn Miller next season. To try and fill this whole they signed free agent Randy Gregory from Dallas. Also on the line is former pro bowler Bradley Chubb. He is yet another Bronco with a suspect injury history, having two great and two injury riddled seasons in his 4 year career. Last year, he played only 7 games due to an ankle injury. If healthy, he and Gregory should represent another ferocious pass rush for the AFC west to deal with, but not necessarily a great run stopping front. Overall, while the Denver defense is somewhat lacking in the rush defense department, they will likely once again be one of the best defenses in the league. 

As for the Broncos as a whole, the picture is less clear. There are many concerns regarding the injury history of many important players, but if everyone is healthy, I believe they are the most well rounded team in the AFC West. There is also a reason for worry for Wilson’s fit. Wilson is of course one of the best quarterbacks of the past decade, but he may need some time to get into a new rhythm on a brand new team for the first time in his career. Additionally, introducing a new quarterback is much more difficult than a receiver (this is why I didn’t have as big of a concern for JuJu, Davante or MVS) because everything runs through the quarterback. In many ways, this Broncos team is similar to Tampa Bay two years ago in having a great roster other than quarterback. Unfortunately for the Broncos, the Bucs had much more leeway to lose earlier in the season and get things going as the playoffs approached. In conclusion, however, I once again see the Broncos as a force to be reckoned with if they can get past the injury and chemistry concerns that I have.

To tie it all together, I would rank the AFC west as Chiefs, Raiders, Chargers and Broncos. This is the same rank the teams fell into last year, and while it may seem like a cop out, I think the most fair assumption is that teams should remain where they are until proven otherwise. Patrick Mahomes and Derek Carr are proven postseason quarterbacks, and while the Chargers and Broncos were the two teams with the most improvement on paper, we’ll just have to wait and see what happens. That being said, if the ranking was flipped upside down I wouldn’t be surprised, because as I’ve said, every team has a legitimate shot at making or even winning the Super Bowl. 










The Secrets Behind the Rams Super Bowl Winning Success

WRITTEN By Gavin Murillo(‘24)

DATA ANALYSIS BY Charlie Neuenschwander(‘24)

ggmuril@usc.edu and cneuensc@usc.edu

Following a legacy defining 23-20 Super Bowl LVI win against the Cincinnati Bengals, Los Angeles is the city of champions once again! For the first time since 1981 the coveted Lombardi Trophy has returned to Hollywood thanks to the efforts of Sean McVay’s high flying offense and dominant defense. Here, we break down how the new look Los Angeles Rams were able to climb to the peak of NFL stardom.

1. The Kupp Connection    

Cooper Kupp was absolutely electric in 2021 averaging 114.5 receiving yards per game with 145 receptions, 1,947 yards, and a league high 16 touchdowns. Being the first player since 2005 to accomplish the triple crown, Kupp was able to cement his legendary season with the accolades of Offensive Player of the Year, Super Bowl MVP, and most importantly, the coveted Super Bowl victory. 

With the gunslinging, no-look-throwing, and dime dropping Quarterback of Matthew Stafford now on his team, Kupp was able to excel reaching never before seen heights as an NFL receiver.  Including the playoffs, Kupp has accumulated over 2,330 receiving yards in total, earning him most single season reception yards in NFL history. 

Scoring 20 touchdowns in total including the postseason, Kupp only trails the Hall of Fame receivers of Randy Moss and the greatest statistical receiver of all time in Jerry Rice. 

Even with the Rams already loaded receiving core of Odell Beckham Jr., Robert Woods, and Van Jefferson, Kupp was able to consistently put up monster numbers earning his place in Super Bowl History. 


2. Defense Wins Championships

This is especially true when you have 285 pound 4x Defensive Player of the Year Aaron Donald and 8x Pro Bowler Von Miller on your team. 

Since the first Super Bowl in 1969,16 of the Super Bowl champions have led the NFL in scoring defense while 13 more finished within the Top 10. Even as the league has shifted towards dynamic, quarterback-driven offensive play, the secret sauce to winning an NFL championship hasn’t been how much a team can score but rather how much they can prevent the other team from scoring. 

Throughout the course of the first ever 17-game NFL season, the Los Angeles Rams allowed only 17 touchdowns in the entire year.  Along with this, the Rams were able to hit their stride when they needed it most, finishing the last two games of playoffs by holding their opponents to a combined total of 0 points in the fourth quarter. 

With Aaron Donald leading defensive tackles in sacks and Von Miller contributing to many of the 50 sacks that ranked the Rams third in that category, the Los Angeles defensive front is scarier, more versatile, and outright more talented than ever.

3. City of Stars

In a city that’s known for it’s Hollywood endings, it is only fitting that the Rams were able to climb to the top of NFL’s Mount Everest and win Super Bowl LVI in their home stadium. Yet unlike many of the Super Bowl Champions of the distant past, the Rams went all in on creating a star studded roster spending over $200 million in cap space.

But, it paid off.

After signing an extension with the Detroit Lions in 2017, Matthew Stafford became the highest paid player in the league, yet saw little success on the field. But for Stafford, it would only take one season to change it all. Signing with the Rams this past summer for a five year, $135 million contract, the former No. 1 pick is now a Super Bowl Champion. 

For Odell Beckham Jr., questions still lingered about his playmaking ability after minimal success in his previous 3 seasons with the Cleveland Browns. Yet much like Stafford, it only took one season in the right system for fans to remember his name, as well as his game. Signing a deal worth up to $4.25 million, Beckham received a $500k signing bonus, $750k guaranteed, and of course an illustrious and legacy defining Super Bowl.

Even after the Blue and Gold Confetti had rained down from the rafters of Sofi Stadium and the rushing flood of cheering fans had left the streets of Los Angeles, a legacy had been defined. A champion had been crowned. And the 2021 Los Angeles Rams would forever be known as Champions of the World.




DeMar I See It DeMar I Like It: DeMar DeRozan's Shot Chart Visualization

By Sunay Sanghani, Class of 2024

SUNAYSAN@USC.EDU

DeMar DeRozan, shooting guard for the Chicago Bulls, will be playing in his fifth All-Star game in the 2021-22 All Star Game in Cleveland. In year 13, it seems like DeRozan is clicking on all cylinders like never before. I want to examine whether DeRozan’s shot selection this season can give some context behind his resurgence.

The most unique aspect of DeRozan’s game is his mid range shooting. In a three-point shooting league, DeRozan thrives off of attempting more mid range shots and attacking the basket. Rightfully so: he currently shoots 54.4% from 10-16 feet range, which is well above the league average. Putting him around floor spacers such as Zach Lavine, Lonzo Ball, and Nikola Vucevic allows DeRozan to dominate the middle of the floor and posession.

Using spatial data and his shot location data available from NBA.com, I created a shot chart visualization to examine the reason behind his success. I extracted the data from the NBA API and created a scatter plot to chart the shot data. Essentially, I created a data frame for every player in the NBA and created a variable for DeMar DeRozan’s season. 

Here’s a snapshot of the base set-up in Python.


Through 48 games in the 2021-2022 season, DeRozan’s shot chart looks like this:

DeRozan is averaging 28.1 PPG on 51.7% shooting percentage in the current 2021-22 campaign.

As expected, DeRozan shoots most of his shots from the midrange and around the paint. Additionally, a majority of his shots are from the (-100,100) coordinates which is quite different from years past. His shots are not as spread out over the court as years past. Any scorer of DeRozan’s ability would typically have shots spread around the entire floor. In particular, one of the most common areas to shoot is around the extended elbow beyond the arc(coordinates 100,200 on the Y-Axis). DeRozan, however, only has a handful in that area. His shots are from the center. Past years confirm that not attempting those extended elbow shots is actually a good thing for DeRozan’s sake. 

For reference, I also created shot charts for the 2020-2021 season and 2019-2020 season. DeRozan was not an All-Star in either of those seasons, but was still considered one of the best players in the league. 2020-21 was considered an off year for DeRozan as his limitations from deep were blatant in the San Antonio Spurs motion offense. He shot 22.7% from deep in his Spurs tenure. Here is the 2020-21 shot chart:

DeRozan averaged 21.6 PPG on 49.5% shooting percentage in 2020-21.

One of the most noticeable differences between the 2020-21 and 2021-22 seasons is the shots around the right elbow area. DeRozan attempted fewer shots from the right side last season than he has this season. The right side has been DeRozan’s strong suit this year: he’s shooting an above-average 46% from the right half of the court. Visually, it can be seen that there is a lot more green from the (0, 200) coordinates(right half) than the (0,-200) coordinates(left half) in the 2021–22 shot chart. He is right-handed and most right-handed players prefer shooting from their dominant side.

Additionally, in both 2019-2020(below) and 2020-2021, he attempted shots from all over beyond-the-arc. His attempts were not as successful and he was pretty limited from there. 

DeRozan averaged 22.1 PPG on 53.7% shooting percentage in 2019-20.

Compared to 2020 and 2021, DeRozan did not attempt as many threes in 2019. He averaged a career low of 0.5 three point attempts per game in 2019-20. In 2021-22, his 3 Point Attempt Average is back up at 1.9 per game. DeRozan’s shot chart for this season suggests he is more surgical in his three-point attempts. He is taking a lot more corner three-point shots(either by design or not), which is a shorter distance at 25 feet compared to the elbow areas. Such a shooting choice could be a big indication for his efficiency: he is not wasting shots on deep attempts.

However, make no mistake about it, DeRozan is also simply shooting the ball better than expected. He is currently shooting 50.1% from the field and averaging 19 shots per game. His three point percentage is at 33.7% which is a career best. For reference, DeRozan is a career 46.3% shooter and usually takes about 15 shots per game. Some credit should go to the Chicago Bulls organization and head coach Billy Donovan to allow DeRozan to attempt more shots and put him in a position to score from his comfort spots.

In year 13, it’s pretty obvious that DeRozan has taken the highs and the lows from his last two seasons in San Antonio to refine his game this year. He’s taking shots from the areas that he usually thrives in: right half of the court and mid range. He has not adapted his game to abide by the common three-point trope in the NBA. He simply does not need to. DeRozan has continued to play his game and it’s working with the Chicago Bulls. As the season progresses, it will be interesting to track whether DeMar DeRozan will be able to use his current shooting model to lead the Bulls to a deep playoff run.

End of Semester 1

This wraps our content for the first semester! This semester was the first semester for the Sports Analytics Division at USC SBA. We focused on doing simple regression analysis with Excel and focused on organizing data on Excel properly. Stay tuned for more projects next semester where we’ll publish our shot chart visualizations we worked on in the first semester and Python visualizations. Thanks!

Is Derrick Henry stiff-arming his way into the MVP race?

BY GAVIN MURILLO

class of 2024, ggmurill@usc.edu

After an impressive 27-3 performance against the Kansas City Chiefs in week 7, Tennessee Titans’ running back Derrick Henry has continued to make a compelling push for the NFL MVP as the W’s continue to pile up. Averaging over 4.6 yards per rushing attempt, Henry has already scored 10 touchdowns this season, rushing for over 100 yards in 5 of his last 6 games. Yet, even with this high powered ground and pound approach, Henry will have to continue his outrageous performance if he hopes to catch up with the quick-footed Kyler Murray, as well as the living legend of Tom Brady in this year's MVP race. 

Since 2001, only three running backs have come away with the coveted MVP title, with Adrian Peterson’s 2012 season marking the last time a non-quarterback has come away with the hardware. However, due to Henry’s prolific performance across the first few weeks of the season, the Titans’ star power rusher is making a strong case both physically and statistically as to why he can be the next great RB to take the cake.

Currently, Henry sits atop the NFL rushing leaderboards with a combined total of 869 rush yards in only 7 games. Along with this, Henry also leads the league in rushing attempts with 191, serving as a clear indication of just how important he is to the success of Tennessee’s overall offensive production. This year alone, Henry has rushed against some of the NFL’s premiere defenses such as the red hot 7-0 Arizona Cardinals, along with the up-and-coming Buffalo Bills led by the emerging star of Quarterback Josh Allen. In both these games combined, Henry rushed for slightly over 200 yards, along with 3 touchdowns respectively. Yet, besides a headscrating OT loss to the New York Jets in early in the season, Henry has outright dominated the lesser defenses of the league at every chance he gets. 

And still, Henry somehow always seems ready to run over whatever person in front of him… regardless of the color of their jersey. Being recognized for his trademark stiff-arm that sends defenders into a different dimension, Henry is no stranger to the physicality of the game. From this season alone, 587 of his 869 yards have come after contact, over 200 yards higher than the league's second-highest rusher, Cleveland Browns RB Nick Chubb. However, even with his prolific physical ability, Henry is still faced with overcoming the obstacle of being a running back, in a league that is progressively shifting towards highly-dynamic, pass-driven quarterback play. 

In the Titans dominant performance over the Kansas City Chiefs this past weekend, Henry accumulated over 86 yards on the ground but did not rush for any touchdowns. Instead, he decided to pass for one! Although oddly specific, this passing touchdown actually significantly increased Henry’s chances of making a run at the coveted MVP title, as the last 8 MVP’s have had at least one passing touchdown. With this daring jump pass that gave the Titans their first score, Henry became only the second player in the Super Bowl era to have both 10 rushing touchdowns and a passing touchdown in the first seven game stretch of the season. Interestingly, the only other player to ever accomplish this feat was none other than an equally as dominant running back, by the name of LaDanian Tomlinson in 2005. A year later, Tomlinson won the 2006 NFL MVP award. 

Some say that history repeats itself, and others say that numbers never lie. But in this rare case of the 6’3 speeding giant fans call “King Henry” both of these assumptions may turn out to be equally as true. While only time will tell whether or not Henry is able to accomplish this feat, one thing is for certain...that stiff-arm is for real. 

Has the Reign of Patrick Mahomes and the Chiefs Come to an End?

Author: Matthew Turner, mtturner@usc.edu

Two weeks ago the once-dominant Kansas City Chiefs were blown out at home by the Buffalo Bills by a score of 38-20. The game never seemed close, with the Chiefs down 24-13 by halftime. Even after the extended weather delay at halftime, the Chiefs couldn’t get anything going. They scraped together just one scoring drive in the last 30 minutes of play: a 1-yard TD reception by Travis Kelce. The Chiefs fell to 2-3, their worst record through 5 games since 2015. While they bounced back next week against the Washington Football Team, a lot of work is still to be done. Have the Kansas City Chiefs and superstar Patrick Mahomes lost their crown for good? Or is this just a bump in the road in route to their 3rd consecutive Super Bowl appearance? Let’s take a closer look at what’s been going on this season.

For starters, the Kansas City Chiefs have played some stiff competition. Through the first 5 games of the season, the Kansas City Chiefs have had the toughest schedule in the NFL. Chiefs opponents are a blistering 17-8 so far this season. This includes games against the division leading Chargers, Ravens, and Bills, who all have a 4-1 record. From this point forward, things look to get easier for Kansas City who has the 14th easiest remaining schedule. They’re in for a few more tough matchups against the division leading Titans, Packers, and Cowboys. However, their remaining matchups against the Giants, Steelers, Broncos, and Bengals help to even things out. They have a pretty average schedule moving forward. Overall, the Chiefs have done an okay job navigating the rapids of these first few games and they look to start building success with calmer water just around the bend.

When evaluating their gameplay, the Chiefs offense has been performing quite well. Their 420.4 yards per game ranks 4th in the NFL and their 30.8 points per game ranks 5th. When compared to last year’s averages of 415.8 yards and 29.6 points per game, it appears as though the Kansas City offense has been full steam ahead to start this season. This conclusion is also supported when looking at Patrick Mahomes’ numbers. When comparing his first 5 games of last season with this season, he actually appears to be performing better. His completion rate is up from 64.6% to 71% and his passing touchdowns increased from 13 to 16. His QBR, passer rating, and passing yards are almost identical to the year before. However, the main issue is in his number of interceptions. Mahomes has thrown 6 interceptions through the first 5 games this season, already tying his mark of 6 interceptions through 16 games last season. In all, Patrick Mahomes is still playing solid football and once he can get his interceptions back under control, it should translate to the win column.

On the other side of the ball, the Chiefs defense has been worse than in years past. Through 5 games they have given up a league high 32.6 points per game and a 2nd worst 437.4 yards per game. The past 2 seasons the Chiefs have fielded one of the better defenses in the NFL, finishing 11th in points per game in 2020 and 7th in points per game in 2019. However, the Chiefs have regressed much to the likes of their defense during Patrick Mahomes’ MVP season in 2018, in which the Chiefs allowed the 2nd most yards per game and the 9th most points per game. Part of this regression can be attributed to the Chiefs difficult strength of schedule. The Chiefs defense has gone against the league's 1st, 6th, 7th, and 9th highest scoring offenses this season. However, even when going against the 16th highest scoring offense, the Philadelphia Eagles, the Chiefs defense was torched for 30 points: 7 points higher than Philadelphia’s scoring average and their second-highest total this season (scored 32 points against Atlanta’s 28th ranked defense). It’s clear that the Kansas City defense has some serious work to do to return to last year’s form. Although Kansas City proved that it’s possible to win without a defense when they made it to the AFC championship game in 2018 on the back of Patrick Mahomes’ MVP season, it’s significantly harder to find long-term success.

If the Chiefs are going to turn things around, they’re going to need all hands on deck. Unfortunately, that might not be possible as several key players are still recovering from various  injuries. Patrick Mahomes’ favorite target and #4 WR in the NFL Tyreek Hill suffered a quad contusion in Sunday’s game against Buffalo. However, leg injuries can linger with wide receivers, so this will be something to watch. Star TE Travis Kelce suffered a stinger late in the game against Buffalo, but it looks like Kelce will be good to go on Sunday which is good news for Kansas City. They’re going to need Kelce more than usual because RB Clyde-Edwards Helaire has an MCL sprain which is going to keep him sidelined for a couple weeks. This is a big hit to the Kansas City run game which currently ranks 7th in the NFL and serves as a nice compliment to their 5th ranked passing attack. 

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