Catching Success: A Deep Dive into NFL Wide Receivers’ Performance
Introduction:
From the September to February of each year, the eyes of the world turn to the grassy war zones of NFL stadiums. 32 teams competing to be the best in the league. In this battle for supremacy, wide receivers play a crucial role, turning catches into game-changing play. Just one spectacular play by a wide receiver may be the difference between a win and a loss for a team. In this article, we take a deep dive into an analysis of NFL wide receiver statistics and show decision-making for their various stakeholders.
Question and Stakeholders:
One of the most important stats associated with a wide receiver is receiving yards. This is a key indicator to tell how good a wide receiver is. However, how does it translate to wins for a team? Wide receivers score touchdowns that equates to 6 points. We will answer the question, do more receiving yards translate to more touchdowns by NFL wide receivers?
The NFL is deeply embedded in the American sports culture. From fans to NFL general managers to sports journalists, statistics drive discussion and decision-making. Many NFL fans play fantasy football and knowing which receivers will score more touchdowns over the course of a season can help them make their draft picks. General managers in the NFL can make informed decisions about which wide receivers to seek out during free-agency to give their team a boost. Sports journalists can seek out this information to provide content for discussion and predictions.
Data Description and Collection:
The dataset utilized in this analysis was compiled from Fantasypros, publicly available NFL player statistics. This is a trusted source by many fantasy football users. It features detailed records of the statistics of every wide receiver that played in the 2023 NFL season, including receiving yards, touchdowns, targets, receptions, yards per reception, and more. The columns from the dataset that are relevant to this analysis include receiving yards and touchdowns. We will be leaving out specific player names because this is a general analysis of wide receivers performance instead of certain player performance. These are the most relevant fields because we want to see if there is a correlation between receiving yards and touchdowns scored.
The data collection process involved a CSV file with all the necessary wide receiver information. Applying Python’s Panda and Matplotlib libraries was crucial in cleaning, manipulating and visualizing the relevant data. This process can help shareholders grasp meaningful insights on NFL wide receivers.
Data Analysis
In this case, Pandas helped to read in the CSV file, take out null values, and manipulate data types. There were two cases where the value of a row was null and to keep the integrity of the data, those rows were taken out. Next, the data type of receiving yards was different from touchdowns. Receiving yards was a string while touchdowns was a float. After matching up the data types to float, I used Python’s Matplotlib to create a scatter plot to visualize receiving yards vs. touchdowns.
The graph presents a scatter plot detailing the relationship between the receiving yards and touchdowns scored by NFL wide receivers in the 2023 season. At first glance, a positive correlation is evident, players with higher number of yards tend to have scored more touchdowns. In terms of football, this makes sense as more yards obtained offers more scoring opportunities.
A concentration of data points can be observed at the lower end of receiving yards, corresponding to fewer touchdowns scored by that player. This may represent the NFL’s role players, who contribute to getting the team first downs and further down the field, but are not the primary offensive targets. The cluster of players becomes thinner as we move right on the receiving yards axis, reflecting that high yardage receivers are less common, thus playing a crucial part on a team.
This graph can offer a great deal of insights for the stakeholders. General managers that have to worry about limiting spending, but also having a competitive offense can use this information during free agency to acquire new players. For example, if Brandin Cooks of the Dallas Cowboys is available, a general manager might be keen on acquiring him because, even though he only had 657 yards in the 2023 season, he scored 8 touchdowns. He might be far cheaper to acquire than someone like D.J. Moore of the Chicago Bears who had 1,364 yards, but also had 8 touchdowns.
Similarly, NFL fans that play fantasy football can use this graph to draft players. A wide receiver in fantasy football gets 0.1 points per receiving yard and 6 points if they score a touchdown. In later rounds of a fantasy draft, a team owner can use this analysis to determine which players will give them an edge. If the fantasy team owner chose two wide receivers that had high receiving yards, but average touchdowns scored, it wouldn’t be a bad idea to draft a player with less yards, but higher touchdowns scored.
Limitations:
This analysis has some limitations since football statistics are swayed by multiple factors. The first lies in the data. These statistics are of the 2023 season only. Each year is different for wide receivers and many things happen to teams during the off-season that might impact a receivers performance the next season. For example, quarterback play is crucial to a wide receiver’s success. If the quarterback is under performing, it is likely that a wide receiver will as well and vice versa. Also, if a team acquires another efficient wide receiver, those two receivers will likely under perform from the year prior since the targets will be split between the two. Finally, injuries can sway these statistics. Injuries to a quarterback or the receivers themselves will surely negatively impact wide receiver performance in terms of touchdowns and receiving yards.
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Dataset:
