Soccer analysis example

This tutorial uses data extracted from video footage of a soccer game that was published in https://github.com/Friends-of-Tracking-Data-FoTD/Last-Row

import numpy as np
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import holoviews as hv
import hvplot.pandas
import matplotlib.pyplot as plt

from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts, dim
from os.path import exists
from urllib.request import urlretrieve

import warnings

warnings.filterwarnings("ignore")

hvplot_defaults = {
    "line_width": 5,
    "frame_height": 350,
    "frame_width": 700,
    "colorbar": True,
    "tiles": None,
    "geo": False,
}

mpd.show_versions()

MovingPandas 0.23.0

SYSTEM INFO
-----------
python     : 3.10.19 | packaged by conda-forge | (main, Jan 26 2026, 23:45:08) [GCC 14.3.0]
executable : /home/anita/miniforge3/envs/mpd-ex/bin/python
machine    : Linux-6.8.0-134-generic-x86_64-with-glibc2.39

PROJ INFO
-----------
PROJ       : 9.6.2
PROJ data dir: /home/anita/miniforge3/envs/mpd-ex/share/proj

PYTHON DEPENDENCIES
-------------------
numpy      : 1.23.1
geopandas  : 1.0.1
geopy      : 2.4.1
geoviews   : 1.15.1
holoviews  : 1.22.1
hvplot     : 0.12.2
mapclassify: 2.8.1
matplotlib : 3.10.8
pandas     : 2.3.3
pyproj     : 3.7.1
shapely    : 2.1.2
stonesoup  : 1.8

Loading soccer dataset from Github

def get_file_from_url(url):
    file = url.split("/")[-1]
    if not exists(file):
        urlretrieve(url, file)
    return file


def get_df_from_gh_url(url):
    file = get_file_from_url(url)
    return pd.read_csv(file)

input_file = "https://raw.githubusercontent.com/Friends-of-Tracking-Data-FoTD/Last-Row/master/datasets/positional_data/liverpool_2019.csv"
df = get_df_from_gh_url(input_file)
df.drop(columns=["Unnamed: 0"], inplace=True)
print(f"Number of records: {len(df)}")

Number of records: 74936

df.head()

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	x	y	z
0	NaN	0.000000	0.000000	NaN	0	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	46.394558	11.134454	0.0
1	NaN	0.185745	1.217580	NaN	1	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	46.580302	12.352034	0.0
2	NaN	0.178659	1.171133	NaN	2	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	46.758961	13.523166	0.0
3	NaN	0.171573	1.124685	NaN	3	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	46.930535	14.647852	0.0
4	NaN	0.164488	1.078238	NaN	4	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	47.095022	15.726090	0.0

From the metadata:

• play: the scoreline after the goal. The team who scored the goal is the one next to the brackets.
• frame: the frame number for the current location. Data provided has 20 frames per second.
• player: the id of the player. The id is consistent within a play but not between plays.
• player_num: the player jersey number. This number is the official one, and did not change for Liverpool in 2019. You can check the corresponding names at this wikipedia link.
• x, y: coordinates for the player/ball. Pitch coordinates go from 0 to 100 on each axis.
• dx, dx: change in (x,y) coordinates from last frame to current frame
• z: height, from 0 to 1.5 (only filled for the ball)
• bgcolor: the main color for the team (used as background color)
• edgecolor the secondary color (used as edge color)

And according to https://en.wikipedia.org/wiki/Football_pitch

the preferred size for many professional teams' stadiums is 105 by 68 metres

plays = list(df.play.unique())


def to_timestamp(row):
    # plays to date
    day = plays.index(row.play) + 1
    start_time = datetime(2019, 1, day, 12, 0, 0)
    # frames to time
    td = timedelta(milliseconds=1000 / 20 * row.frame)
    return start_time + td


# frame: the frame number for the current location. Data provided has 20 frames per second
df["time"] = df.apply(to_timestamp, axis=1)
df.set_index("time", inplace=True)

# the preferred size for many professional teams' stadiums is 105 by 68 metres, accoring to https://en.wikipedia.org/wiki/Football_pitch
pitch_length = 105
pitch_width = 68
df.x = df.x / 100 * pitch_length
df.y = df.y / 100 * pitch_width

df

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	x	y	z
time
2019-01-01 12:00:00.000	NaN	0.000000	0.000000	NaN	0	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	48.714286	7.571429	0.0
2019-01-01 12:00:00.050	NaN	0.185745	1.217580	NaN	1	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	48.909318	8.399383	0.0
2019-01-01 12:00:00.100	NaN	0.178659	1.171133	NaN	2	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	49.096909	9.195753	0.0
2019-01-01 12:00:00.150	NaN	0.171573	1.124685	NaN	3	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	49.277061	9.960539	0.0
2019-01-01 12:00:00.200	NaN	0.164488	1.078238	NaN	4	Liverpool [3] - 0 Bournemouth	0	NaN	NaN	49.449774	10.693741	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...
2019-01-19 12:00:06.000	blue	0.000000	0.000000	white	120	Leicester 0 - [3] Liverpool	10267	NaN	defense	103.661067	36.529840	0.0
2019-01-19 12:00:06.050	blue	0.000000	0.000000	white	121	Leicester 0 - [3] Liverpool	10267	NaN	defense	103.661067	36.529840	0.0
2019-01-19 12:00:06.100	blue	0.000000	0.000000	white	122	Leicester 0 - [3] Liverpool	10267	NaN	defense	103.661067	36.529840	0.0
2019-01-19 12:00:06.150	blue	0.000000	0.000000	white	123	Leicester 0 - [3] Liverpool	10267	NaN	defense	103.661067	36.529840	0.0
2019-01-19 12:00:06.200	blue	0.000000	0.000000	white	124	Leicester 0 - [3] Liverpool	10267	NaN	defense	103.661067	36.529840	0.0

74936 rows × 12 columns

df["team"].value_counts().plot(title="team", kind="bar", figsize=(15, 3))

<Axes: title={'center': 'team'}, xlabel='team'>

df["player_num"].value_counts().plot(title="player_num", kind="bar", figsize=(15, 3))

<Axes: title={'center': 'player_num'}, xlabel='player_num'>

df["team"] = df["team"].astype("category").cat.as_ordered()
df["player"] = df["player"].astype("category").cat.as_ordered()
df["player_num"] = df["player_num"].astype("category").cat.as_ordered()

Finally, let's create trajectories:

Trajectories

%%time
CRS = None
tc = mpd.TrajectoryCollection(df, "player", x="x", y="y", crs=CRS)
mpd.TemporalSplitter(tc).split(mode="day")
print(f"Finished creating {len(tc)} trajectories")

Finished creating 364 trajectories
CPU times: user 2.6 s, sys: 12.9 ms, total: 2.61 s
Wall time: 2.61 s

pitch = Polygon(
    [(0, 0), (0, pitch_width), (pitch_length, pitch_width), (pitch_length, 0), (0, 0)]
)
plotted_pitch = GeoDataFrame(
    pd.DataFrame([{"geometry": pitch, "id": 1}]), crs=CRS
).hvplot(color="white", alpha=0.5)

plotted_pitch * tc.filter("player_num", 20).hvplot(**hvplot_defaults)

Plays

PLAY = 2
title = f"Play {PLAY} {plays[PLAY]}"
play_trajs = tc.filter("play", plays[PLAY])
play_trajs

TrajectoryCollection with 20 trajectories

Trajectory 15 (No. rows: 440 | Length: 200.0 unknown units)

Start: 2019-01-03 12:00:00	End: 2019-01-04 12:00:12.800000	Duration: 1 day, 0:00:12.800000
Bounds: (6.551567770977525, 3.7750702170347226, 82.09398284322187, 38.26729953820284)		CRS: None
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry
time
2019-01-03 12:00:00.000	red	-0.043670	0.074173	white	0	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (35.21733 3.77507)
2019-01-03 12:00:00.050	red	-0.047653	0.070251	white	1	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (35.1673 3.82284)
2019-01-03 12:00:00.100	red	-0.051604	0.066492	white	2	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (35.11311 3.86806)
2019-01-03 12:00:00.150	red	-0.055524	0.062895	white	3	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (35.05481 3.91082)
2019-01-03 12:00:00.200	red	-0.059413	0.059461	white	4	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (34.99243 3.95126)

Trajectory 1417 (No. rows: 183 | Length: 28.3 unknown units)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (2.4067528263372275, 32.64796726872315, 29.011220461407955, 36.247053914056096)		CRS: None
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry
time
2019-01-03 12:00:00.000	red	-0.166623	0.035776	white	0	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (29.01122 35.94375)
2019-01-03 12:00:00.050	red	-0.161728	0.028071	white	1	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (28.84141 35.96284)
2019-01-03 12:00:00.100	red	-0.156964	0.020572	white	2	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (28.67659 35.97683)
2019-01-03 12:00:00.150	red	-0.152331	0.013280	white	3	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (28.51665 35.98586)
2019-01-03 12:00:00.200	red	-0.147829	0.006194	white	4	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (28.36143 35.99007)

Trajectory 1726 (No. rows: 183 | Length: 42.1 unknown units)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (5.734167253361569, 17.619044936607835, 38.25362992582764, 32.38861292505991)		CRS: None
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry
time
2019-01-03 12:00:00.000	red	-0.235714	0.157618	white	0	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (38.25363 23.11031)
2019-01-03 12:00:00.050	red	-0.234865	0.137702	white	1	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (38.00702 23.20395)
2019-01-03 12:00:00.100	red	-0.234039	0.118320	white	2	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (37.76128 23.2844)
2019-01-03 12:00:00.150	red	-0.233236	0.099472	white	3	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (37.51638 23.35205)
2019-01-03 12:00:00.200	red	-0.232455	0.081157	white	4	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (37.2723 23.40723)

Trajectory 2150 (No. rows: 183 | Length: 49.7 unknown units)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (-0.14078795317397683, 20.167864811566734, 37.73409725043924, 39.63452549724319)		CRS: None
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry
time
2019-01-03 12:00:00.000	red	0.035577	-0.179075	white	0	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.6906 39.63453)
2019-01-03 12:00:00.050	red	0.024562	-0.186474	white	1	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.71639 39.50772)
2019-01-03 12:00:00.100	red	0.013744	-0.193706	white	2	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.73082 39.376)
2019-01-03 12:00:00.150	red	0.003124	-0.200771	white	3	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.7341 39.23948)
2019-01-03 12:00:00.200	red	-0.007300	-0.207669	white	4	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.72643 39.09826)

Trajectory 2813 (No. rows: 183 | Length: 54.6 unknown units)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (2.7660926033158155, 10.256982134264762, 52.57416878737574, 30.905891049635116)		CRS: None
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry
time
2019-01-03 12:00:00.000	red	-0.133693	0.009661	white	0	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (52.57417 10.25698)
2019-01-03 12:00:00.050	red	-0.141654	0.016081	white	1	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (52.42543 10.26792)
2019-01-03 12:00:00.100	red	-0.149469	0.022388	white	2	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (52.26849 10.28314)
2019-01-03 12:00:00.150	red	-0.157138	0.028583	white	3	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (52.10349 10.30258)
2019-01-03 12:00:00.200	red	-0.164663	0.034666	white	4	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (51.9306 10.32615)

... and 15 more trajectories

play_trajs.plot(column="team", colormap={"attack": "hotpink", "defense": "turquoise"})

<Axes: >

generalized = mpd.MinTimeDeltaGeneralizer(play_trajs).generalize(
    tolerance=timedelta(seconds=0.5)
)

generalized.add_speed()

TrajectoryCollection with 20 trajectories

Trajectory 15 (No. rows: 46 | Length: 19228.5 km)

Start: 2019-01-03 12:00:00	End: 2019-01-04 12:00:12.800000	Duration: 1 day, 0:00:12.800000
Bounds: (6.621318386672574, 3.7750702170347226, 82.09398284322187, 38.22377088649229)		CRS: epsg:4326
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64), speed (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry	speed
time
2019-01-03 12:00:00.000	red	-0.043670	0.074173	white	0	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (35.21733 3.77507)	173009.429041
2019-01-03 12:00:00.500	red	-0.082083	0.042270	white	10	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (34.53424 4.15101)	173009.429041
2019-01-03 12:00:01.000	red	-0.117349	0.026626	white	20	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (33.46598 4.37082)	242113.267888
2019-01-03 12:00:01.500	red	-0.149470	0.027239	white	30	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (32.04559 4.54504)	317629.439724
2019-01-03 12:00:02.000	red	-0.178445	0.044111	white	40	Fulham 0 - [1] Liverpool	15	10.0	attack	0.0	POINT (30.3061 4.78425)	389611.812608

Trajectory 1417 (No. rows: 20 | Length: 2645.1 km)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (2.4171798590630593, 32.64796726872315, 29.011220461407955, 36.21487965556477)		CRS: epsg:4326
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64), speed (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry	speed
time
2019-01-03 12:00:00.000	red	-0.166623	0.035776	white	0	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (29.01122 35.94375)	268877.797805
2019-01-03 12:00:00.500	red	-0.123573	-0.031987	white	10	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (27.52165 35.92202)	268877.797805
2019-01-03 12:00:01.000	red	-0.093635	-0.079111	white	20	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (26.40839 35.51668)	220626.580808
2019-01-03 12:00:01.500	red	-0.076808	-0.105597	white	30	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (25.53375 34.86809)	214688.615696
2019-01-03 12:00:02.000	red	-0.073093	-0.111445	white	40	Fulham 0 - [1] Liverpool	1417	11.0	attack	0.0	POINT (24.76008 34.11658)	219082.498957

Trajectory 1726 (No. rows: 20 | Length: 4381.7 km)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (5.734167253361569, 17.692516247713808, 38.25362992582764, 32.329922320985105)		CRS: epsg:4326
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64), speed (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry	speed
time
2019-01-03 12:00:00.000	red	-0.235714	0.157618	white	0	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (38.25363 23.11031)	504443.403156
2019-01-03 12:00:00.500	red	-0.228249	-0.017532	white	10	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (35.82371 23.49713)	504443.403156
2019-01-03 12:00:01.000	red	-0.223058	-0.139336	white	20	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (33.45904 22.89244)	502341.047061
2019-01-03 12:00:01.500	red	-0.220141	-0.207795	white	30	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (31.13574 21.65899)	551301.696136
2019-01-03 12:00:02.000	red	-0.219497	-0.222910	white	40	Fulham 0 - [1] Liverpool	1726	20.0	attack	0.0	POINT (28.82995 20.15953)	583426.888212

Trajectory 2150 (No. rows: 20 | Length: 5186.7 km)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (-0.11124068481597718, 20.180051254744626, 37.69059626103843, 39.63452549724319)		CRS: epsg:4326
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64), speed (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry	speed
time
2019-01-03 12:00:00.000	red	0.035577	-0.179075	white	0	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.6906 39.63453)	330032.029258
2019-01-03 12:00:00.500	red	-0.065693	-0.245552	white	10	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (37.46222 38.15883)	330032.029258
2019-01-03 12:00:01.000	red	-0.147225	-0.295339	white	20	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (36.2845 36.29351)	463799.633169
2019-01-03 12:00:01.500	red	-0.209018	-0.328435	white	30	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (34.36468 34.15206)	589859.110405
2019-01-03 12:00:02.000	red	-0.251072	-0.344841	white	40	Fulham 0 - [1] Liverpool	2150	9.0	attack	0.0	POINT (31.91004 31.84798)	686732.475941

Trajectory 2813 (No. rows: 20 | Length: 5785.1 km)

Start: 2019-01-03 12:00:00	End: 2019-01-03 12:00:09.100000	Duration: 0:00:09.100000
Bounds: (2.780061514910607, 10.256982134264762, 52.57416878737574, 30.89351462425568)		CRS: epsg:4326
Columns: bgcolor (object), dx (float64), dy (float64), edgecolor (object), frame (int64), play (object), player (int64), player_num (category), team (category), z (float64), speed (float64)

Data preview

	bgcolor	dx	dy	edgecolor	frame	play	player	player_num	team	z	geometry	speed
time
2019-01-03 12:00:00.000	red	-0.133693	0.009661	white	0	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (52.57417 10.25698)	407784.977209
2019-01-03 12:00:00.500	red	-0.206756	0.068807	white	10	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (50.73586 10.55018)	407784.977209
2019-01-03 12:00:01.000	red	-0.265280	0.116731	white	20	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (48.21435 11.20359)	569995.811624
2019-01-03 12:00:01.500	red	-0.309266	0.153435	white	30	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (45.1623 12.14093)	697093.847659
2019-01-03 12:00:02.000	red	-0.338712	0.178918	white	40	Fulham 0 - [1] Liverpool	2813	26.0	attack	0.0	POINT (41.73236 13.2859)	786909.255594

... and 15 more trajectories

generalized.hvplot(
    title=title, c="speed", hover_cols=["player", "team"], **hvplot_defaults
)

(
    plotted_pitch
    * generalized.hvplot(
        title=title, c="speed", hover_cols=["player"], cmap="Viridis", **hvplot_defaults
    )
)

get_file_from_url(
    "https://github.com/movingpandas/movingpandas/raw/main/tutorials/data/soccer_field.png"
)

pitch_img = hv.RGB.load_image(
    "soccer_field.png", bounds=(0, 0, pitch_length, pitch_width)
)
(
    pitch_img
    * generalized.hvplot(
        title=title,
        c="team",
        colormap={"attack": "limegreen", "defense": "purple"},
        hover_cols=["team"],
        **hvplot_defaults
    )
    * generalized.get_start_locations().hvplot(label="start", color="orange")
)

(
    pitch_img
    * generalized.hvplot(title=title, c="team", hover_cols=["team"], **hvplot_defaults)
    * generalized.get_start_locations().hvplot(
        label="start",
        c="team",
        hover_cols=["team"],
        colormap={"attack": "limegreen", "defense": "purple"},
        colorbar=True,
        legend=True,
    )
)

Continue exploring MovingPandas

1. Bird migration analysis
2. Ship data analysis
3. Horse collar data exploration
4. OSM traces
5. Soccer game
6. Mars rover & heli
7. Ever Given
8. Iceberg
9. Pollution data