Introduction

Column

Basic Info

An Analysis Of How Well High School Football Ratings Predict Future Success

The purpose of this study is to analyze ratings for high school football recruits as well as their success during their college careers. I will also be studying how highly these players were drafted by teams in the National Football League. NFL teams start scouting players before they are eligible to be drafted, and the earlier they start watching them, the more useful intel they can gather. This study can help see if rankings for these players at the high school level give a good indication of their true potential in the future, allowing NFL teams to identify top prospects earlier.

I will be using one dataset in this presentation. It includes 439 players from various positions that were recruited between the years 2009-2018.

This presentation looks to answer the following questions:

Does a higher rating coming out of high school lead to more future success and higher draft status?
Do certain positions develop more talent than others?
Which schools tend to produce high draft picks?

I will answer these questions by finding trends in the data using visual exploratory analysis.

Glimpse of Player Analysis

rank	name	recruit year	recruit rating	stars	recruited position	school	draft grade	draft year	overall	round	pick	drafted position	draft success	countable plays	average PPA	total PPA	pass PPA	rush PPA
15	Andre Debose	2009	0.9913	5	Wide Receiver	Florida	30	2015	221	7	4	Wide Receiver	0	7	-0.166	-1.163	-3.399	2.236
19	Aaron Murray	2009	0.9900	5	Quarterback	Georgia	62	2014	163	5	23	Quarterback	0	352	0.336	118.289	107.423	10.866
31	Tajh Boyd	2009	0.9840	5	Quarterback	Clemson	39	2014	213	6	37	Quarterback	0	481	0.262	126.105	105.782	20.323
52	Logan Thomas	2009	0.9719	4	Quarterback	Virginia Tech	61	2014	120	4	20	Quarterback	0	483	0.231	111.505	114.490	-2.985
87	AJ McCarron	2009	0.9594	4	Quarterback	Alabama	78	2014	164	5	24	Quarterback	0	320	0.295	94.326	88.052	6.274
148	Arthur Lynch	2009	0.9300	4	Tight End	Georgia	45	2014	155	5	15	Tight End	0	28	1.144	32.036	32.036	NA
227	Carlos Hyde	2009	0.9078	4	Fullback	Ohio State	88	2014	57	2	25	Running Back	1	207	0.306	63.250	5.726	57.524
240	Kevin Norwood	2009	0.9051	4	Wide Receiver	Alabama	66	2014	123	4	23	Wide Receiver	0	31	1.372	42.531	42.531	NA
244	Jeremy Gallon	2009	0.9043	4	Athlete	Michigan	30	2014	244	7	29	Wide Receiver	0	87	1.068	92.933	94.315	-1.382
286	Tyler Gaffney	2009	0.8955	4	Fullback	Stanford	56	2014	204	6	28	Running Back	0	320	0.115	36.796	3.081	33.715

Column

Explanation of Variables

Below are descriptions of the variables used in this study:

rank: player’s national ranking in their class coming out of high school

name: player’s name

recruit year: year player started college football

recruit rating: composite recruit rating (0.0-1.0)

stars: recruit star rating (1-5)

recruited position: position player was recruited as

school: college player attended

draft grade: assessment of NFL potential (0-100)

draft year: year player was drafted

overall: overall pick number player was selected at in draft

round: which round player was selected at in draft (1-7)

pick: pick number in round player was selected at in draft

drafted position: position player was drafted as

draft success: was player drafted in rounds 1, 2, or 3? (0 = no, 1 = yes)

countable plays: how many plays player contributed to that was tracked

average PPA: Predicted Points Added, how many points per play player added to their team

total PPA: how many points in their career player added to their team

pass PPA: how many points player added on passing plays in their career

rush PPA: how many points player added on rushing plays in their career

Abstract

From my analysis, I found that better high school ratings do positively correlate with players becoming early draft picks. I also found out that position value matters, as players at more valuable positions are drafted more often in early rounds than less valuable positions, even if they are not necessarily more talented. Finally, there is a strong positive correlation between the amount of top recruits a school can bring into their program and the amount of early draft picks they produce.

Player Overview

Column

High School Recruits Table

NFL Draft Prospects Table

Player Stats Table

Stars

Column

Stars Breakdown

2 Stars

3 Stars

4 Stars

5 Stars

Comparison

Column

Analysis

I started by creating a bar chart breaking down how many players were in each category of stars received. 3 and 4 stars were the most prevalent, with 189 and 167, respectively. This allows us to see how the majority of players are rated coming out of high school.

Then, I made a separate bar chart for each star rating, analyzing where the players in those categories were selected in the NFL draft. Most of the 2 stars were taken in the back half of the draft, with a mode in the 6th round. 3 stars were more evenly distributed, with less of a skew and two modes in the 4th and 6th rounds. The 4 stars were also very evenly distributed, with their highest round being the 3rd. Finally, the overwhelming majority of 5 stars were selected very early in the draft, having a mode in the 2nd round.

Comparing all of the star ratings and the rounds their players were selected in, it is clear that the players with higher ratings coming out of high school, while not always better, do tend to be drafted higher than those with lower ratings.

Positions

Column

Stars Breakdown Per Drafted Position

Draft Success Per Position

Column

Analysis

I began by generating a bar chart that allowed me to see what the star rating percentage was for each position. Then, I made another bar chart which showed the percentage of players taken early in the draft at each position. This allows us to see if there’s any correlation between the amount of highly rated players there are at a position and how early they may be drafted, indicating if some positions may have an easier transition from high school to college than others.

When looking at draft success, quarterbacks have the best percentage of high draft picks at over 55%. That could have been predicted based on their star ratings, because outside of cornerbacks(which has the lowest sample size), quarterbacks have the highest percentage of 5 stars, indicating their players were ranked very highly coming out of high school.

However, running backs, which had the second highest 5 star percentage outside of cornerbacks, had the least draft success of just under 38% being taken in the early rounds. This seems to defy the trend of high ratings being drafted early. It shows that while there may be quality players at that position, NFL teams care more about what position a player is at than just how talented they are, as a quarterback is much more valuable to a team than a running back.

Colleges

Column

Colleges with Top Recruits

Colleges with Top Draft Picks

Comparison

Colleges with Most Top Recruits	Colleges to Produce Most Top Picks
Alabama	Alabama
Clemson	Ohio State
Ohio State	Penn State
Notre Dame	Clemson
Florida	Notre Dame
Georgia	LSU
Stanford	USC
Auburn	Florida
Florida State	Ole Miss
Michigan	Stanford

Column

Analysis

In this section, I focused on which colleges were able to add the highest amount of top recruits, and also which colleges produced the most early round draft picks. To start this, I classified a top recruit as a 4 or 5 star in addition to an early round pick being a 1st, 2nd, or 3rd rounder.

After filtering the data to only include 4 or 5 star recruits, I found that Alabama had gained the most amount of top recruits with 18, or about 4% of all of the 4 and 5 stars, with Clemson and Ohio State following. Then, after filtering the data to only include 1st, 2nd, and 3rd rounders, Alabama was again in the lead with 17 selections, again representing almost 4% of all early picks, with Ohio State and Penn State following.

This shows that Alabama is not only the best in recent years at recruiting the top high school players in the nation, but also the best at developing their talent and producing top NFL prospects. Other schools, such as Ohio State, Clemson, and Notre Dame, also do a good job of this, but Alabama is cleatly the frontrunner in this area.

Conclusion

Column

Results

From my analysis, I was able to determine that high school rankings, while not perfect, do give a good indication of which players may become high draft picks. The higher a player’s rating, the more likely they are to end up being an early draft pick. This can allow NFL teams to start scouting these players multiple years before they are eligible to be drafted.

I also found that while the top rated players can be some of the most talented players, it does not always mean they will be drafted highly. NFL teams care about positions, so a more valuable position like quarterback will be drafted more often in the early rounds than a less valuable position like running back, even if they are not as talented.

Finally, I was able to see which schools are the best at getting the top recruits to play for them and which schools are the best at producing top draft picks. Alabama was clearly at the top of both categories, and there is a correlation between the amount of highly rated recruits a school gets and how many early draft picks they have. This can show NFL teams which schools to scout more often, as they have the best talent, and it also shows recruits which schools they should attend, as it reveals which programs are the best at developing their talent.

Limitations

One of the limitations of this dataset was that it mainly focused on offensive skill players. It did not place any emphasis on linemen or defensive players, so we are unsure if the results of how high school ratings trasnlate to on success also apply to those positions.

Another limitation was that some players had a lot less countable plays than others, which would hurt their PPA. That piece of analysis is not always an accurate read of a player’s impact, as there are ways to be involved with having the ball in their hands. That is why some players with lower PPA’s may have been drafted much earlier than those with higher PPA’s, making those variables and countable plays a hard stat to use effectively in this study.

References

https://www.kaggle.com/datasets/jwblackston/recruit-draft-eval

About the Author

Column {data-width = 650}

About Me

My name is John Hannan, and I am an undergraduate student at the University of Dayton, currently in my junior year and on track to graduate in May 2025.

I am pursuing a major in Sport Management as well as minors in both Business Administration and Data Analytics.

After graduation, I am interested in working in the football industry as a scout for an NFL franchise, finding players who excel at the college level and will help teams win at the professional level.

I am eager to continue to find new trends to try to make scouting players more efficient and accurate.

Please connect with me on LinkedIn.

Column {.tabset data-width = 600}

Picture

---
title: "Football Recruiting Analysis"
output:
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: materia
      primary: "#F54242"
      secondary: "#2196f3"
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{=html}
<style>
.chart-title {  /* chart_title  */
   font-size: 20px;
  }
body{ /* Normal  */
      font-size: 20px;
  }
</style>
<head>
    <base target="_blank">
</head>

```
```{css color tabs}
.nav-tabs-custom .nav-tabs > li > a 
  {
    color: #black;
  } 

.nav-tabs-custom .nav-tabs > li.active > a 
  {
    color: #2196f3;
  } 

/* To set color on hover */
.nav-tabs-custom .nav-tabs > li.active > a:hover 
  {
    color: grey;
  }

<style type="text/css"> .sidebar 
  { 
    overflow: auto; 
  } 
</style>
```

```{r setup, include=FALSE}
library(flexdashboard)
```

```{r package_data}
library(tidyverse)
library(knitr)
library(dplyr)
library(readr)
library(ggplot2)
library(DT)
library(plotly)
library(viridis)

high_school_rankings <- read_csv("C:/Users/John Hannan/Downloads/archive (3).zip")
high_school_rankings <- subset(high_school_rankings, select = -c(ident, college, id, team, season, career_avgPPA, career_totalPPA, position...24))
high_school_rankings <- high_school_rankings %>% rename("recruit year" = "recruit_year",
                                                        "recruit rating" = "comp_recruit_rating",
                                                        "recruited position" = "position...7",
                                                        "draft grade" = "draft_grade",
                                                        "draft year" = "draft_year",
                                                        "round" = "d_round",
                                                        "drafted position" = "position_y",
                                                        "draft success" = "draft_success",
                                                        "countable plays" = "countablePlays",
                                                        "average PPA" = "averagePPA.all",
                                                        "total PPA" = "totalPPA.all",
                                                        "pass PPA" = "totalPPA.pass",
                                                        "rush PPA" = "totalPPA.rush")

high_school_rankings$round <- as.factor(high_school_rankings$round)
high_school_rankings$stars <- as.factor(high_school_rankings$stars)

high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "APB"] <- "Running Back"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "ATH"] <- "Athlete"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "CB"] <- "Cornerback"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "DUAL"] <- "Quarterback"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "FB"] <- "Fullback"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "ILB"] <- "Inside Linebacker"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "OLB"] <- "Outside Linebacker"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "PRO"] <- "Quarterback"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "RB"] <- "Running Back"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "S"] <- "Safety"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "SDE"] <- "Strong Defensive End"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "TE"] <- "Tight End"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "WDE"] <- "Weak Defensive End"
high_school_rankings$`recruited position`[high_school_rankings$`recruited position` == "WR"] <- "Wide Receiver"
```

# Introduction

## Column {.tabset data-width="650"}

### Basic Info

<font size = 5> **An Analysis Of How Well High School Football Ratings Predict Future Success** </font>

The purpose of this study is to analyze ratings for high school football recruits as well as their success during their college careers. I will also be studying how highly these players were drafted by teams in the National Football League. NFL teams start scouting players before they are eligible to be drafted, and the earlier they start watching them, the more useful intel they can gather. This study can help see if rankings for these players at the high school level give a good indication of their true potential in the future, allowing NFL teams to identify top prospects earlier.

I will be using one dataset in this presentation. It includes 439 players from various positions that were recruited between the years 2009-2018.

This presentation looks to answer the following questions:

-   Does a higher rating coming out of high school lead to more future success and higher draft status?

-   Do certain positions develop more talent than others?

-   Which schools tend to produce high draft picks?

I will answer these questions by finding trends in the data using visual exploratory analysis.

### Glimpse of Player Analysis

```{r glimpse}
kable(high_school_rankings[1:10,])
```

## Column {data-height="650"}

### Explanation of Variables

Below are descriptions of the variables used in this study:

rank: player's national ranking in their class coming out of high school

name: player's name

recruit year: year player started college football

recruit rating: composite recruit rating (0.0-1.0)

stars: recruit star rating (1-5)

recruited position: position player was recruited as

school: college player attended

draft grade: assessment of NFL potential (0-100)

draft year: year player was drafted

overall: overall pick number player was selected at in draft

round: which round player was selected at in draft (1-7)

pick: pick number in round player was selected at in draft

drafted position: position player was drafted as

draft success: was player drafted in rounds 1, 2, or 3? (0 = no, 1 = yes)

countable plays: how many plays player contributed to that was tracked

average PPA: Predicted Points Added, how many points per play player added to their team

total PPA: how many points in their career player added to their team

pass PPA: how many points player added on passing plays in their career

rush PPA: how many points player added on rushing plays in their career

### Abstract

From my analysis, I found that better high school ratings do positively correlate with players becoming early draft picks. I also found out that position value matters, as players at more valuable positions are drafted more often in early rounds than less valuable positions, even if they are not necessarily more talented. Finally, there is a strong positive correlation between the amount of top recruits a school can bring into their program and the amount of early draft picks they produce.

# Player Overview

## Column {.tabset}

### High School Recruits Table

```{r recruits table}
DT::datatable(high_school_rankings[,1:6], rownames = FALSE, 
              options = list(columnDefs = list(list(className = 'dt-center', targets = c(0, 2:5)))))
```

### NFL Draft Prospects Table

```{r prospects table}
DT::datatable(high_school_rankings[,c(2, 7:14)], rownames = FALSE, 
              options = list(columnDefs = list(list(className = 'dt-center', targets = c(1, 8)))))
```

### Player Stats Table

```{r stats table}
DT::datatable(high_school_rankings[,c(2, 15:19)], rownames = FALSE, 
              options = list(columnDefs = list(list(className = 'dt-center', targets = c(1:5)))))
```

Stars
===

Column {.tabset data-width=850 .no-padding}
-----

### Stars Breakdown

```{r stars}
font <- list(
    family = "Arial",
    size = 14,
    color = "white"
)
label <- list(
    bgcolor = "#232F34",
    bordercolor = "transparent",
    font = font
)


p1 <- ggplot(high_school_rankings, aes(x = stars)) +
  geom_bar(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "Number of Players per Star Ratings", x = "Star Rating", y = "Number of Players")

ggplotly(p1) %>% 
    style(hoverlabel = label) %>% 
  layout(font = font) 
```

### 2 Stars

```{r 2 stars}

stars2 <- high_school_rankings %>% 
  filter(stars == 2)
p2 <- ggplot(stars2, aes(x = round)) +
  geom_bar(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "2 Stars Taken per Round", x = "Round", y = "Number of Players")
ggplotly(p2)
```

### 3 Stars

```{r 3 stars}

stars3 <- high_school_rankings %>% 
  filter(stars == 3)
p3 <- ggplot(stars3, aes(x = round)) +
  geom_bar(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "3 Stars Taken per Round", x = "Round", y = "Number of Players")
ggplotly(p3)
```

### 4 Stars

```{r 4 stars}

stars4 <- high_school_rankings %>% 
  filter(stars == 4)
p4 <- ggplot(stars4, aes(x = round)) +
  geom_bar(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "4 Stars Taken per Round", x = "Round", y = "Number of Players")
ggplotly(p4)
```

### 5 Stars

```{r 5 stars}

stars5 <- high_school_rankings %>% 
  filter(stars == 5)
p5 <- ggplot(stars5, aes(x = round)) +
  geom_bar(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "5 Stars Taken per Round", x = "Round", y = "Number of Players")
ggplotly(p5)
```

### Comparison

```{r conditional}
p_c <- ggplot(high_school_rankings, aes(x = stars, fill = round)) + 
  geom_bar(width=0.75, position = "fill") +
  scale_y_continuous(breaks = seq(0, 1, by = .2), 
                     labels = scales::percent) + 
  labs(y = "Round Percentage") +
  theme(axis.text.x = element_text(angle=30, hjust=1),
        text = element_text(size = 20))

ggplotly(p_c) %>% 
  style(hoverlabel = label) %>% 
  layout(font = font) 
```

Column
-----------------------------------------------------------------------

### Analysis

I started by creating a bar chart breaking down how many players were in each category of stars received. 3 and 4 stars were the most prevalent, with 189 and 167, respectively. This allows us to see how the majority of players are rated coming out of high school.

Then, I made a separate bar chart for each star rating, analyzing where the players in those categories were selected in the NFL draft. Most of the 2 stars were taken in the back half of the draft, with a mode in the 6th round. 3 stars were more evenly distributed, with less of a skew and two modes in the 4th and 6th rounds. The 4 stars were also very evenly distributed, with their highest round being the 3rd. Finally, the overwhelming majority of 5 stars were selected very early in the draft, having a mode in the 2nd round.

Comparing all of the star ratings and the rounds their players were selected in, it is clear that the players with higher ratings coming out of high school, while not always better, do tend to be drafted higher than those with lower ratings.

Positions
===

Column {.tabset data-width=650}
-----

### Stars Breakdown Per Drafted Position

```{r DP}
p_dp <- ggplot(high_school_rankings, aes(x = `drafted position`, fill = stars)) + 
  geom_bar(width=0.75, position = "fill") +
  scale_y_continuous(breaks = seq(0, 1, by = .2), 
                     labels = scales::percent) + 
  labs(y = "Star Rating Percent") +
  theme(axis.text.x = element_text(angle=30, hjust=1),
        text = element_text(size = 20))

ggplotly(p_dp) %>% 
  style(hoverlabel = label) %>% 
  layout(font = font) 
```

### Draft Success Per Position

```{r DS}
high_school_rankings$`draft success` <- as.factor(high_school_rankings$`draft success`)
p_ds <- ggplot(high_school_rankings, aes(x = `drafted position`, fill = `draft success`)) + 
  geom_bar(width=0.75, position = "fill") +
  scale_y_continuous(breaks = seq(0, 1, by = .2), 
                     labels = scales::percent) + 
  labs(y = "Draft Success Percent") +
  theme(axis.text.x = element_text(angle=30, hjust=1),
        text = element_text(size = 20))

ggplotly(p_ds) %>% 
  style(hoverlabel = label) %>% 
  layout(font = font) 
```

Column
-----------------------------------------------------------------------

### Analysis

I began by generating a bar chart that allowed me to see what the star rating percentage was for each position. Then, I made another bar chart which showed the percentage of players taken early in the draft at each position. This allows us to see if there's any correlation between the amount of highly rated players there are at a position and how early they may be drafted, indicating if some positions may have an easier transition from high school to college than others. 

When looking at draft success, quarterbacks have the best percentage of high draft picks at over 55%. That could have been predicted based on their star ratings, because outside of cornerbacks(which has the lowest sample size), quarterbacks have the highest percentage of 5 stars, indicating their players were ranked very highly coming out of high school. 

However, running backs, which had the second highest 5 star percentage outside of cornerbacks, had the least draft success of just under 38% being taken in the early rounds. This seems to defy the trend of high ratings being drafted early. It shows that while there may be quality players at that position, NFL teams care more about what position a player is at than just how talented they are, as a quarterback is much more valuable to a team than a running back.

Colleges
===

Column {.tabset data-width=650}
-----

### Colleges with Top Recruits

```{r college}

top_colleges <- high_school_rankings %>% 
  filter(stars %in% c(4, 5)) %>% 
  group_by(school) %>% 
  summarise(count = n(), percent = round(n()/nrow(high_school_rankings)*100, 2)) %>% 
  arrange(desc(count))

datatable(top_colleges)

```

### Colleges with Top Draft Picks


```{r nfl}

top_picks <- high_school_rankings %>% 
  filter(`draft success`==1) %>% 
  group_by(school) %>% 
  summarise(count = n(), percent = round(n()/nrow(high_school_rankings)*100, 2)) %>% 
  arrange(desc(count))

datatable(top_picks)

```

### Comparison

```{r}

kable(cbind.data.frame(top_colleges$school[1:10], top_picks$school[1:10]),
      col.names = c("Colleges with Most Top Recruits", "Colleges to Produce Most Top Picks"))
```

Column
-----------------------------------------------------------------------

### Analysis

In this section, I focused on which colleges were able to add the highest amount of top recruits, and also which colleges produced the most early round draft picks. To start this, I classified a top recruit as a 4 or 5 star in addition to an early round pick being a 1st, 2nd, or 3rd rounder.

After filtering the data to only include 4 or 5 star recruits, I found that Alabama had gained the most amount of top recruits with 18, or about 4% of all of the 4 and 5 stars, with Clemson and Ohio State following. Then, after filtering the data to only include 1st, 2nd, and 3rd rounders, Alabama was again in the lead with 17 selections, again representing almost 4% of all early picks, with Ohio State and Penn State following. 

This shows that Alabama is not only the best in recent years at recruiting the top high school players in the nation, but also the best at developing their talent and producing top NFL prospects. Other schools, such as Ohio State, Clemson, and Notre Dame, also do a good job of this, but Alabama is cleatly the frontrunner in this area.

Conclusion
===

Column {data-length=650}
---

### Results

From my analysis, I was able to determine that high school rankings, while not perfect, do give a good indication of which players may become high draft picks. The higher a player's rating, the more likely they are to end up being an early draft pick. This can allow NFL teams to start scouting these players multiple years before they are eligible to be drafted. 

I also found that while the top rated players can be some of the most talented players, it does not always mean they will be drafted highly. NFL teams care about positions, so a more valuable position like quarterback will be drafted more often in the early rounds than a less valuable position like running back, even if they are not as talented.

Finally, I was able to see which schools are the best at getting the top recruits to play for them and which schools are the best at producing top draft picks. Alabama was clearly at the top of both categories, and there is a correlation between the amount of highly rated recruits a school gets and how many early draft picks they have. This can show NFL teams which schools to scout more often, as they have the best talent, and it also shows recruits which schools they should attend, as it reveals which programs are the best at developing their talent.

### Limitations

One of the limitations of this dataset was that it mainly focused on offensive skill players. It did not place any emphasis on linemen or defensive players, so we are unsure if the results of how high school ratings trasnlate to on success also apply to those positions.

Another limitation was that some players had a lot less countable plays than others, which would hurt their PPA. That piece of analysis is not always an accurate read of a player's impact, as there are ways to be involved with having the ball in their hands. That is why some players with lower PPA's may have been drafted much earlier than those with higher PPA's, making those variables and countable plays a hard stat to use effectively in this study.

### References

https://www.kaggle.com/datasets/jwblackston/recruit-draft-eval

About the Author
===

Column {data-width = 650}
---

### About Me

My name is John Hannan, and I am an undergraduate student at the University of Dayton, currently in my junior year and on track to graduate in May 2025. 

I am pursuing a major in Sport Management as well as minors in both Business Administration and Data Analytics.

After graduation, I am interested in working in the football industry as a scout for an NFL franchise, finding players who excel at the college level and will help teams win at the professional level. 

I am eager to continue to find new trends to try to make scouting players more efficient and accurate. 

Please connect with me on [LinkedIn](https://www.linkedin.com/in/john-hannan-a1897b222/).

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