How does Cam Ward stack up?

tennessee titans
How does Cam Ward compare to this year’s rookies and historical Titans QBs
Author

chicken

Published

December 26, 2025

Housekeeping

Import dependencies and set options

# chickenstats library and utilities
from chickenstats.chicken_nhl._helpers import charts_directory

import nflreadpy as nfl

import polars as pl


# plotting library and utilities
import matplotlib.pyplot as plt
import seaborn as sns

# miscellaneous utilities
from pathlib import Path
pl.Config.set_tbl_cols(-1)
pl.Config.set_tbl_rows(30)
polars.config.Config

Create directory for charts

charts_directory()

chickenstats matplotlib styles

plt.style.use("chickenstats")  # this is available when you import chickenstats.utilities

Downloading and prepping data

Player stats

seasons = list(range(2002, 2026))

player_stats = nfl.load_player_stats(seasons=seasons)
conditions = (pl.col("player_id") != "0", pl.col("player_name").is_not_null())

player_stats = player_stats.filter(conditions)

Rosters

rosters = nfl.load_rosters_weekly(seasons=seasons)
rosters = rosters.filter(pl.col("status") == "ACT")

Adding years of experience

columns = (pl.col("season"), pl.col("gsis_id").alias("player_id"), pl.col("week"), pl.col("years_exp"))

merge_columns = ["season", "player_id", "week"]

player_stats = player_stats.join(rosters.select(columns), on=merge_columns)

Aggregating to season level

group_columns = [
    "player_id",
    "player_name",
    "player_display_name",
    "position",
    "position_group",
    "season",
    "season_type",
    "team",
    "years_exp",
]

group_columns = (pl.col(x) for x in group_columns)

sum_columns = [
    "completions",
    "attempts",
    "passing_yards",
    "passing_tds",
    "passing_interceptions",
    "sacks_suffered",
    "sack_yards_lost",
    "sack_fumbles",
    "sack_fumbles_lost",
    "passing_air_yards",
    "passing_yards_after_catch",
    "passing_first_downs",
    "passing_epa",
    "passing_2pt_conversions",
    "carries",
    "rushing_yards",
    "rushing_tds",
    "rushing_fumbles",
    "rushing_fumbles_lost",
    "rushing_first_downs",
    "rushing_epa",
    "rushing_2pt_conversions",
    "receptions",
    "targets",
    "receiving_yards",
    "receiving_tds",
    "receiving_fumbles",
    "receiving_fumbles_lost",
    "receiving_air_yards",
    "receiving_yards_after_catch",
    "receiving_first_downs",
    "receiving_epa",
    "receiving_2pt_conversions",
    "special_teams_tds",
    "def_tackles_solo",
    "def_tackles_with_assist",
    "def_tackle_assists",
    "def_tackles_for_loss",
    "def_tackles_for_loss_yards",
    "def_fumbles_forced",
    "def_sacks",
    "def_sack_yards",
    "def_qb_hits",
    "def_interceptions",
    "def_interception_yards",
    "def_pass_defended",
    "def_tds",
    "def_fumbles",
    "def_safeties",
    "misc_yards",
    "fumble_recovery_own",
    "fumble_recovery_yards_own",
    "fumble_recovery_opp",
    "fumble_recovery_yards_opp",
    "fumble_recovery_tds",
    "penalties",
    "penalty_yards",
    "punt_returns",
    "punt_return_yards",
    "kickoff_returns",
    "kickoff_return_yards",
    "fg_made",
    "fg_att",
    "fg_missed",
    "fg_blocked",
    "fg_long",
    "fg_made_0_19",
    "fg_made_20_29",
    "fg_made_30_39",
    "fg_made_40_49",
    "fg_made_50_59",
    "fg_made_60_",
    "fg_missed_0_19",
    "fg_missed_20_29",
    "fg_missed_30_39",
    "fg_missed_40_49",
    "fg_missed_50_59",
    "fg_missed_60_",
    "pat_made",
    "pat_att",
    "pat_missed",
    "pat_blocked",
    "pat_pct",
    "gwfg_made",
    "gwfg_att",
    "gwfg_missed",
    "gwfg_blocked",
    "gwfg_distance",
    "fantasy_points",
    "fantasy_points_ppr",
]

sum_columns = (pl.col(x).sum().alias(f"{x}_sum") for x in sum_columns)

mean_columns = ["passing_cpoe", "target_share", "air_yards_share", "pacr", "racr", "wopr"]

mean_columns = (pl.col(x).mean().alias(f"{x}_mean") for x in mean_columns)

agg_columns = (*sum_columns, *mean_columns)


player_stats = player_stats.group_by(group_columns).agg(agg_columns)

Getting QB stats

conditions = (
    pl.col("player_id") != "0",
    pl.col("player_name").is_not_null(),
    pl.col("position") == "QB",
    pl.col("attempts_sum") >= 1,
    pl.col("season_type") == "REG",
    pl.col("years_exp") == 0,
)

qb_stats = player_stats.filter(conditions)
qb_stats = qb_stats.with_columns(epa_per_attempt=pl.col("passing_epa_sum") / pl.col("attempts_sum"))

Plotting data

Setting team and other values

team = "TEN"
light_blue = "#4B92DB"
navy = "#002244"
red = "#C60C30"
gray = "#D3D3D3"

Scatter plot

# Setting overall figures
fig, ax = plt.subplots(dpi=650, figsize=(8, 5))

# Aesthetics, likes the tight layout and despining axes
fig.tight_layout()
sns.despine()

# Setting the x, y, and z values

x_col = "attempts_sum"
y_col = "passing_epa_sum"
size_col = "passing_tds_sum"

# Getting the averages and drawing the average lines
x_mean = qb_stats.select(pl.col(x_col)).mean().item(0, 0)
y_mean = qb_stats.select(pl.col(y_col)).mean().item(0, 0)

ax.axvline(x=x_mean, zorder=-1, alpha=0.5)
ax.axhline(y=y_mean, zorder=-1, alpha=0.5)

# Setting the size norm so bubbles are consistent across figures
size_min = qb_stats.select(pl.col(size_col)).min().item(0, 0)
size_max = qb_stats.select(pl.col(size_col)).max().item(0, 0)

size_norm = (size_min, size_max)
sizes = (20, 150)

# Filtering data and plotting the non-selected teams first
plot_data = qb_stats.filter(pl.col("team") != team)

# They all get gray colors
facecolor = gray
edgecolor = "white"

# Plotting the non-selected teams' data
sns.scatterplot(
    x=plot_data[x_col],
    y=plot_data[y_col],
    size=plot_data[size_col],
    sizes=sizes,
    size_norm=size_norm,
    lw=1.5,
    facecolor=facecolor,
    edgecolor=edgecolor,
    alpha=0.5,
    legend=False,
)

# Filtering the data and plotting the selected team
plot_data = qb_stats.filter(pl.col("team") == team)

# Setting the colors
facecolor = light_blue
edgecolor = navy

# Plotting the selected teams' data
sns.scatterplot(
    x=plot_data[x_col],
    y=plot_data[y_col],
    size=plot_data[size_col],
    sizes=sizes,
    size_norm=size_norm,
    lw=1.5,
    facecolor=facecolor,
    edgecolor=edgecolor,
    alpha=0.5,
    legend=False,
)

# Labeling Cam Ward

player_list = [
    "C.Ward",
    "P.Mahomes",
    "R.Wilson",
    "J.Burrow",
    "D.Prescott",
    "J.Herbert",
    "R.Griffin",
    "M.Ryan",
    "C.Stroud",
    "J.Daniels",
    "B.Roethlisberger",
    "M.Jones",
    "C.Newton",
    "J.Burrow",
    "J.Winston",
    "B.Purdy",
    "B.Nix",
    "B.Mayfield",
    "D.Watson",
    "C.Kessler",
    "M.Penix",
    "D.Lock",
    "A.Luck",
    "K.Allen",
    "K.Cousins",
    "J.Milton",
    "T.Lance",
    "T.Tebow",
    "J.Locker",
    "M.Mariota",
    "W.Levis",
    "R.Smith",
    "Z.Mettenberger",
    "M.Willis",
    "V.Young",
    "B.Young",
    "J.Rosen",
    "D.Carr",
    "B.Bortles",
    "D.Kizer",
]

label_data = qb_stats.filter(pl.col("player_name").is_in(player_list), pl.col("years_exp") == 0)

x_offset = -50
y_offset = -15

for player_data in label_data.iter_rows(named=True):
    x_position = player_data[x_col] + x_offset
    y_position = player_data[y_col] + y_offset

    arrow_props = {"arrowstyle": "simple", "linewidth": 0.25, "color": "tab:gray"}

    # Plotting the annotation
    ax.annotate(
        text=f"{player_data['player_display_name']}",
        xy=(player_data[x_col], player_data[y_col]),
        xytext=(x_position, y_position),
        fontsize=6,
        bbox={"facecolor": "white", "alpha": 0.5, "edgecolor": "white", "pad": 0},
        arrowprops=arrow_props,
    )

# Iterating through the dataframe to label the bubbles
# for row, player in plot_data.iterrows():
#     # Setting x and y positions that are slightly offset from the data they point to
#     x_position = player.xga_p60 + 0.25
#     y_position = player.xgf_p60 - 0.25

#     # Annotation options
#     arrow_props = {"arrowstyle": "simple", "linewidth": 0.25, "color": "tab:gray"}

#     # Plotting the annotation
#     ax.annotate(
#         text=f"{player.player}",
#         xy=(player.xga_p60, player.xgf_p60),
#         xytext=(x_position, y_position),
#         fontsize=6,
#         bbox={"facecolor": "white", "alpha": 0.5, "edgecolor": "white", "pad": 0},
#         arrowprops=arrow_props,
#     )

# Setting axis lables
ax.axes.set_xlabel("Total Passing Attempts")
ax.axes.set_ylabel("Total Passing EPA")

# Setting figure suptitle and subtitle
fig_suptitle = "Title"
fig.suptitle(fig_suptitle, x=0.01, y=1.08, fontsize=11, fontweight="bold", horizontalalignment="left")

subtitle = "Total Passing EPA and Attempts, Sized for Passing TDs | NFL Rookie Seasons 2002-25"
fig.text(s=subtitle, x=0.01, y=1.02, fontsize=10, horizontalalignment="left")

# Attribution
attribution = "Data & EPA model: nflreadpy python package  | Viz @chickenandstats"
fig.text(s=attribution, x=0.99, y=-0.05, fontsize=8, horizontalalignment="right", style="italic")

# Save figure
savepath = Path(f"./charts/epa_attempt_attempts_{team}.png")
# fig.savefig(savepath, transparent=False, bbox_inches="tight")

qb_stats.filter(pl.col("player_name").str.contains("V.Young"))
# qb_stats.sort("passing_epa_sum", descending=True).head(25)["player_name"].unique().to_list()
qb_stats.filter(pl.col("years_exp") == 0).sort("passing_epa_sum", descending=True).tail(25)
shape: (25, 106)
player_id player_name player_display_name position position_group season season_type team years_exp completions_sum attempts_sum passing_yards_sum passing_tds_sum passing_interceptions_sum sacks_suffered_sum sack_yards_lost_sum sack_fumbles_sum sack_fumbles_lost_sum passing_air_yards_sum passing_yards_after_catch_sum passing_first_downs_sum passing_epa_sum passing_2pt_conversions_sum carries_sum rushing_yards_sum rushing_tds_sum rushing_fumbles_sum rushing_fumbles_lost_sum rushing_first_downs_sum rushing_epa_sum rushing_2pt_conversions_sum receptions_sum targets_sum receiving_yards_sum receiving_tds_sum receiving_fumbles_sum receiving_fumbles_lost_sum receiving_air_yards_sum receiving_yards_after_catch_sum receiving_first_downs_sum receiving_epa_sum receiving_2pt_conversions_sum special_teams_tds_sum def_tackles_solo_sum def_tackles_with_assist_sum def_tackle_assists_sum def_tackles_for_loss_sum def_tackles_for_loss_yards_sum def_fumbles_forced_sum def_sacks_sum def_sack_yards_sum def_qb_hits_sum def_interceptions_sum def_interception_yards_sum def_pass_defended_sum def_tds_sum def_fumbles_sum def_safeties_sum misc_yards_sum fumble_recovery_own_sum fumble_recovery_yards_own_sum fumble_recovery_opp_sum fumble_recovery_yards_opp_sum fumble_recovery_tds_sum penalties_sum penalty_yards_sum punt_returns_sum punt_return_yards_sum kickoff_returns_sum kickoff_return_yards_sum fg_made_sum fg_att_sum fg_missed_sum fg_blocked_sum fg_long_sum fg_made_0_19_sum fg_made_20_29_sum fg_made_30_39_sum fg_made_40_49_sum fg_made_50_59_sum fg_made_60__sum fg_missed_0_19_sum fg_missed_20_29_sum fg_missed_30_39_sum fg_missed_40_49_sum fg_missed_50_59_sum fg_missed_60__sum pat_made_sum pat_att_sum pat_missed_sum pat_blocked_sum pat_pct_sum gwfg_made_sum gwfg_att_sum gwfg_missed_sum gwfg_blocked_sum gwfg_distance_sum fantasy_points_sum fantasy_points_ppr_sum passing_cpoe_mean target_share_mean air_yards_share_mean pacr_mean racr_mean wopr_mean epa_per_attempt
str str str str str i32 str str i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 f64 i32 i32 i32 i32 i32 i32 i32 f64 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 f64 i32 i32 i32 i32 i32 i32 i32 i32 f64 f64 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 i32 f64 i32 i32 i32 i32 i32 f64 f64 f64 f64 f64 f64 f64 f64 f64
"00-0035652" "R.Finley" "Ryan Finley" "QB" "QB" 2019 "REG" "CIN" 0 41 87 474 2 2 11 -93 4 3 721 203 20 -52.636759 0 10 77 0 0 0 5 0.280104 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 24.66 24.66 -15.687838 0.0 0.0 0.65507 null 0.0 -0.60502
"00-0027950" "C.Ponder" "Christian Ponder" "QB" "QB" 2011 "REG" "MIN" 0 158 291 1853 13 13 30 -164 5 2 2667 831 91 -56.446926 0 28 219 0 1 0 15 17.573881 0 0 0 0 0 0 0 0 0 0 0.0 0 0 2 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 118.02 118.02 -7.464352 0.0 0.0 0.691941 null 0.0 -0.193976
"00-0026993" "J.Freeman" "Josh Freeman" "QB" "QB" 2009 "REG" "TB" 0 159 291 1857 10 18 19 -102 5 1 2649 658 89 -58.254535 1 31 162 0 4 1 15 2.430892 1 0 0 1 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 5 7 0 0 0 3 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 94.58 94.58 -4.683177 0.0 0.0 0.733723 0.0 0.0 -0.200187
"00-0030565" "G.Smith" "Geno Smith" "QB" "QB" 2013 "REG" "NYJ" 0 247 443 3046 12 21 43 -315 4 3 3963 1260 143 -59.77581 0 72 366 6 4 1 28 14.142214 0 1 1 13 0 0 0 13 0 1 1.309993 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 4 0 0 0 5 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 193.74 194.74 -4.97212 0.002604 0.002871 0.874442 1.0 0.005916 -0.134934
"00-0036971" "T.Lawrence" "Trevor Lawrence" "QB" "QB" 2021 "REG" "JAX" 0 359 602 3641 12 17 32 -238 6 5 4738 1634 178 -60.74786 2 73 334 2 2 0 23 11.314142 0 0 0 0 0 0 0 0 0 0 0.0 0 0 2 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 199.04 199.04 -4.281167 0.0 0.0 0.822605 null 0.0 -0.10091
"00-0031280" "D.Carr" "Derek Carr" "QB" "QB" 2014 "REG" "LV" 0 348 599 3270 21 12 24 -149 4 2 4664 1462 165 -61.50931 0 29 92 0 5 2 15 -11.191634 0 0 1 0 0 0 0 17 0 0 -0.504618 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 4 8 0 0 0 9 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 192.0 192.0 -5.982209 0.001179 0.002426 0.730652 0.0 0.003467 -0.102687
"00-0040398" "B.Cook" "Brady Cook" "QB" "QB" 2025 "REG" "NYJ" 0 77 131 679 1 7 18 -118 2 1 794 385 30 -62.011141 0 10 42 0 1 0 5 -3.80431 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 2 3 0 0 0 1 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 19.36 19.36 -8.473645 0.0 0.0 0.903789 null 0.0 -0.473367
"00-0036945" "J.Fields" "Justin Fields" "QB" "QB" 2021 "REG" "CHI" 0 159 270 1870 7 10 36 -264 8 4 2645 683 83 -64.928772 0 72 420 2 4 1 26 6.727855 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 4 6 0 0 0 2 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 126.8 126.8 -1.590425 0.0 0.0 0.768913 null 0.0 -0.240477
"00-0026898" "M.Sanchez" "Mark Sanchez" "QB" "QB" 2009 "REG" "NYJ" 0 196 363 2444 12 20 26 -195 8 3 3407 997 123 -71.400733 2 36 106 3 2 0 18 7.100561 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 2 2 0 0 0 8 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 132.36 132.36 -5.202597 0.0 0.0 0.775397 null 0.0 -0.196696
"00-0039376" "S.Rattler" "Spencer Rattler" "QB" "QB" 2024 "REG" "NO" 0 130 228 1317 4 5 22 -136 4 3 1834 642 63 -75.739327 0 18 146 0 1 0 8 13.575403 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 67.28 67.28 -10.31886 0.0 0.0 0.750064 null 0.0 -0.33219
"00-0024408" "B.Gradkowski" "Bruce Gradkowski" "QB" "QB" 2006 "REG" "TB" 0 177 328 1661 9 9 25 -146 7 3 2241 794 79 -84.508159 0 41 161 0 4 3 14 -7.189951 0 0 0 0 0 0 0 0 0 0 0.0 0 0 2 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 4 0 0 0 2 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 88.54 88.54 -4.785349 NaN 0.0 1.13587 null NaN -0.257647
"00-0023436" "A.Smith" "Alex Smith" "QB" "QB" 2005 "REG" "SF" 0 84 165 875 1 11 29 -185 9 2 0 875 37 -85.504377 0 30 103 0 2 1 5 -11.978286 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 6 8 0 0 0 4 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 21.3 21.3 null NaN NaN 0.0 null NaN -0.518208
"00-0033106" "J.Goff" "Jared Goff" "QB" "QB" 2016 "REG" "LA" 0 112 205 1089 5 7 26 -222 5 2 1409 572 47 -86.709483 0 8 16 1 0 0 2 0.238419 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 53.16 53.16 -11.053336 0.0 0.0 0.837851 null 0.0 -0.422973
"00-0021141" "J.Harrington" "Joey Harrington" "QB" "QB" 2002 "REG" "DET" 0 215 429 2294 12 16 8 -75 1 1 166 2294 110 -86.779541 0 7 4 0 1 0 1 -8.771017 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 106.16 106.16 null 0.0 NaN 24.639077 null NaN -0.202283
"00-0029531" "R.Lindley" "Ryan Lindley" "QB" "QB" 2012 "REG" "ARI" 0 89 171 752 0 7 12 -91 2 2 1406 262 38 -87.941983 0 4 7 0 1 0 2 -2.622497 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 12.78 12.78 -12.828982 0.0 0.0 0.574158 null 0.0 -0.514281
"00-0027659" "J.Clausen" "Jimmy Clausen" "QB" "QB" 2010 "REG" "CAR" 0 157 299 1558 3 9 33 -223 1 0 1974 813 73 -92.992759 0 23 57 0 7 2 10 -22.119157 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 8 0 0 0 2 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 58.02 58.02 -12.385229 0.0 0.0 0.798837 null 0.0 -0.311013
"00-0020562" "C.Hutchinson" "Chad Hutchinson" "QB" "QB" 2002 "REG" "DAL" 0 127 250 1555 7 8 34 -265 12 8 137 1555 63 -94.5604 0 18 74 0 0 0 3 -4.270921 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 65.6 65.6 null 0.0 NaN 18.174022 null NaN -0.378242
"00-0037013" "Z.Wilson" "Zach Wilson" "QB" "QB" 2021 "REG" "NYJ" 0 213 383 2334 9 11 44 -370 4 1 2885 1099 112 -95.573186 1 29 185 4 0 0 10 7.612972 1 0 1 0 0 0 0 8 0 0 -0.740859 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 3 0 0 0 1 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 151.86 151.86 -9.820057 0.003497 0.004558 0.896501 0.0 0.008436 -0.249538
"00-0027948" "B.Gabbert" "Blaine Gabbert" "QB" "QB" 2011 "REG" "JAX" 0 210 413 2214 12 11 40 -293 5 3 3216 1135 110 -101.764242 0 48 98 0 8 1 17 -14.178523 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 10 0 0 0 3 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 116.36 116.36 -8.846946 0.0 0.0 0.803466 null 0.0 -0.246403
"00-0033899" "D.Kizer" "DeShone Kizer" "QB" "QB" 2017 "REG" "CLE" 0 255 476 2894 11 22 38 -226 6 5 4272 1440 130 -112.868852 0 77 419 5 3 1 34 18.41577 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 2 5 0 0 0 3 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 175.66 175.66 -5.950583 0.0 0.0 0.722045 null 0.0 -0.237119
"00-0040676" "C.Ward" "Cam Ward" "QB" "QB" 2025 "REG" "TEN" 0 320 537 3117 15 7 55 -410 9 7 3887 1513 151 -113.529241 0 37 148 1 1 0 15 7.221071 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 1 2 0 0 0 1 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 177.48 177.48 -3.848776 0.0 0.0 0.84424 null 0.0 -0.211414
"00-0031407" "B.Bortles" "Blake Bortles" "QB" "QB" 2014 "REG" "JAX" 0 280 475 2908 11 17 55 -345 4 0 3326 1620 132 -113.703585 1 56 419 0 3 1 30 14.894275 0 0 0 0 0 0 0 0 0 0 0.0 0 0 2 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 3 4 0 0 0 6 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 168.22 168.22 -7.378702 0.0 0.0 0.961041 null 0.0 -0.239376
"00-0020608" "D.Carr" "David Carr" "QB" "QB" 2002 "REG" "HOU" 0 233 444 2592 9 15 76 -411 8 3 211 2592 117 -132.459271 2 59 282 3 13 4 28 -8.950429 0 0 0 0 0 0 0 0 0 0 0.0 0 0 2 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 12 20 0 0 0 12 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 145.88 145.88 null 0.0 NaN 5.264255 null NaN -0.298332
"00-0034343" "J.Rosen" "Josh Rosen" "QB" "QB" 2018 "REG" "ARI" 0 217 393 2278 11 14 45 -320 7 5 3262 977 112 -150.632712 1 23 138 0 3 0 10 3.882554 0 0 0 0 0 0 0 0 0 0 0.0 0 0 1 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 7 8 0 0 0 1 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 112.92 112.92 -8.322272 0.0 0.0 0.748037 null 0.0 -0.383289
"00-0039150" "B.Young" "Bryce Young" "QB" "QB" 2023 "REG" "CAR" 0 315 527 2877 11 10 62 -477 9 6 4009 1300 135 -159.273442 1 39 253 0 2 0 20 22.6693 1 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 4 4 0 0 0 2 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0 0 0 0 0 156.38 156.38 -1.251331 0.0 0.0 0.799165 null 0.0 -0.302227 
qb_stats["season"].unique().to_list()
[2002,
 2003,
 2004,
 2005,
 2006,
 2007,
 2008,
 2009,
 2010,
 2011,
 2012,
 2013,
 2014,
 2015,
 2016,
 2017,
 2018,
 2019,
 2020,
 2021,
 2022,
 2023,
 2024,
 2025]