Summary
Football analytics is undergoing a transformation with predictive player labeling and coordinate data, enhancing how we understand player performance and team strategies. Key Points:
- Predictive player labeling accurately identifies players, allowing for detailed analysis of individual skills beyond traditional stats.
- Coordinate data captures exact player movements on the field, offering insights into optimal positioning and tactics.
- Advanced computational techniques like AI optimize player movements in real time, improving tactical decisions.
Sports Analytics: Transforming Sports through Data-Driven Insights
Sports analytics has revolutionized the way teams approach strategy and performance optimization. By harnessing data from various sources—including match footage, player tracking systems, and sensor data—analysts can create a unified platform that offers a comprehensive view of all relevant metrics. Advanced visualization techniques such as interactive dashboards and 3D visualizations enable coaches and analysts to delve into complex datasets with ease, facilitating more informed decision-making.Moreover, predictive analytics and machine learning have become indispensable in forecasting game outcomes, evaluating player performance, and formulating team strategies. These models are meticulously trained on historical data and are continuously refined to provide actionable insights. This ongoing process ensures that teams stay ahead of the curve by identifying potential areas for improvement well before issues become critical.
By integrating these sophisticated tools into their workflows, sports organizations can not only enhance their strategic planning but also achieve a competitive edge in their respective arenas. The fusion of robust data integration with cutting-edge visualization methods empowers stakeholders at all levels to make data-driven decisions that translate into tangible results on the field.
Xg in Football: Understanding its Value and Shortcomings
Expected Goals (xG) has become an essential metric in modern football analytics, offering insights into the quality of scoring opportunities created by teams. However, it's crucial to interpret xG with a degree of caution due to its inherent limitations. One significant factor that xG does not account for is luck. For instance, a team may dominate a match and generate a much higher xG than their opponents but still end up losing due to exceptional goalkeeping or simply missing clear chances.Moreover, xG can profoundly influence team tactics and strategy. By analyzing their xG data, teams can assess the effectiveness of their current tactics and determine areas needing improvement. For example, if a team's xG is consistently lower than expected, it might indicate inefficiencies in their attacking approach. Consequently, the coaching staff might consider modifying their offensive strategies to enhance goal-scoring opportunities.
By understanding both the strengths and limitations of xG, teams can make more informed decisions on and off the pitch. While it serves as a valuable tool for evaluating performance trends and tactical approaches, acknowledging external factors like luck ensures a more holistic interpretation of match outcomes.
Key Points Summary
Insights & Summary
- StatsBomb`s course covers the basics of football analytics, including xG and opposition tactics.
- Driblab tailors player analysis and performance metrics to each club`s specific needs.
- More than 100 individual metrics are recorded per player, such as goals, shots, duels, and passes.
- Eric Eager and Richard Erickson provide a clear introduction to statistical models for analyzing football data in their book.
- Football Benchmark offers insights using financial, operational, social media, and player valuation data from over 200 sources.
- Modern sports analytics leverage computer learning methods to derive meaningful insights from precise player tracking data.
Football analytics is becoming increasingly sophisticated with courses like StatsBomb`s offering foundational knowledge on key concepts. Companies like Driblab customize their analysis based on each club`s unique needs. With over 100 metrics tracked per player, there`s a wealth of data available for deep dives. Books by experts help beginners understand statistical models used in the field. Tools like Football Benchmark use diverse datasets for comprehensive insights. Modern techniques also include advanced computer learning methods for precise tracking data analysis.
Extended Comparison:| Provider | Focus Area | Key Features | Latest Trends/Authoritative Views |
|---|---|---|---|
| StatsBomb | Football Analytics Basics | Covers xG and opposition tactics; over 100 individual metrics per player recorded such as goals, shots, duels, passes. | Increasing use of machine learning for predictive analytics. |
| Driblab | Tailored Player Analysis | Customized performance metrics to each club's specific needs. | Growing demand for personalized analytical solutions in football clubs. |
| Eric Eager & Richard Erickson (Book) | Statistical Models Introduction | Clear introduction to statistical models for analyzing football data. | Rising importance of understanding advanced statistical methods among analysts. |
| Football Benchmark | Comprehensive Insights | Uses financial, operational, social media, and player valuation data from over 200 sources. | 'Moneyball' approach gaining traction with a focus on financial and operational efficiency. |
| Modern Sports Analytics Methods | Player Tracking Data Insights | Leverages computer learning methods for meaningful insights from precise tracking data. | 'AI-driven analysis' becoming mainstream with real-time decision-making capabilities. |
Conversely, the significance of player positioning—specifically where players without the ball (the opposing team) are located—is evident. However, there currently seems to be no standardized metric or concept that effectively captures this aspect.
Contextualizing Player Performance: Beyond Traditional Metrics
In the realm of sports analytics, a comprehensive evaluation of player performance goes beyond merely tracking on-ball actions. One critical aspect often overlooked is the value of off-ball movements in team play. These subtle yet impactful actions can significantly disrupt defensive setups, create open shooting lanes, and facilitate effective decoy plays that lead to scoring opportunities. Traditional metrics tend to undervalue these contributions because they are challenging to quantify directly.Moreover, an accurate assessment of play necessitates contextual analysis. Current scoring systems predominantly emphasize the actions of the player with possession, neglecting crucial elements such as defender positioning, movement timing, and overall team strategy. By integrating contextual factors into play evaluation frameworks, we can achieve a more nuanced understanding of each player's true impact on the game.
This dual approach—recognizing off-ball movements and incorporating context—provides a richer and more precise measurement of performance. Advanced metrics should be developed to capture these dimensions fully, thereby offering insights that reflect the complete scope of a player's contribution to their team's success.
This emerging trend is evident in football play data. Renowned for their detailed analytics, companies like Opta and Wyscout dominate the commercial landscape. However, for those seeking publicly accessible comprehensive data, StatsBomb’s open-data stands out as a leading resource. While I won't delve deeply into the specifics here, it's worth noting that almost every aspect of ball touches throughout a match can be tracked. For instance, by following the provided procedure at this link, we can generate visualizations of ball touches and shooting actions in particular scenarios.
To illustrate further, let’s consider how one can map out ball interactions during a game using these tools. The outlined process at the given link enables us to create visual representations of various play elements such as shots and passes within specific contexts. This ability to see precise movements not only enhances our understanding but also opens new avenues for tactical analysis and fan engagement.
Ultimately, while commercial entities like Opta and Wyscout offer premium services with extensive datasets, StatsBomb's contribution through its open-data initiative democratizes access to critical football metrics. By adhering to the steps mentioned in the linked guide, enthusiasts and analysts alike can harness this information to draw insightful conclusions about on-field performance patterns.
What sets StatsBomb apart is its innovative use of "360 Data" in recent match analyses. This advanced feature not only tracks the players interacting with the ball but also captures the positions of those off-ball, likely through video analysis. This comprehensive approach enables a deeper evaluation of a player's effectiveness even when they don't have possession, providing insights into "quality movement". Such detailed analytics can be instrumental in assessing performance and determining market value for both individual players and teams.
Nevertheless, there's a reason I mentioned "theoretically": While StatsBomb's 360 Data provides the positions of players who don't have possession of the ball, it doesn't allow us to identify which specific players are at those coordinates. In essence, this means we can't determine who is located where at any given moment, nor can we track what actions that player was taking in the moments before and after that snapshot.
One challenge we face is the difficulty in consistently acquiring specific elements, such as back numbers, to identify players while analyzing live game footage. This issue isn't unique to StatsBomb; it's a common problem across the sports analytics industry. Given that most players worldwide don't wear high-precision GPS devices during matches, there's a pressing need for a reliable system to pinpoint individual player positions using available video footage.
To address this widespread issue, developing a mechanism that can accurately determine player locations from live videos would be invaluable. Such technology could revolutionize how data is collected and analyzed in sports, offering insights even in the absence of sophisticated tracking equipment. By focusing on refining these identification techniques, we can significantly enhance our understanding of game dynamics and player performance across various levels of play globally.
Advanced Computational Techniques in Sports Analytics: Optimizing Player Movements and Predicting Positions
In the realm of sports analytics, optimizing player movements and predicting player positions are critical for strategic planning. The linear programming transport problem is a sophisticated method used to minimize the total distance traveled by players between frames. This approach ensures that the constraints of the visible area and observed coordinates are respected, leading to more efficient gameplay analysis.Notably, when analyzing match data such as that from StatsBomb 360, there are instances where not all players' IDs are recorded in every frame. To address this gap, the ball carrier's Player ID is propagated to adjacent frames. This technique allows analysts to predict Player IDs for those not explicitly recorded, enhancing the accuracy and completeness of player tracking throughout the game sequence.
These methods collectively contribute to a deeper understanding of player dynamics on the field, enabling teams to make informed decisions based on precise movement patterns and predicted positions. By integrating advanced computational techniques with real-time data analysis, sports strategists can significantly improve their tactical approaches and overall team performance.
First, data sourced from StatsBomb is imported into a Pandas DataFrame. Each game's information is stored in a separate file, identified by its unique game_id.
import pandas df_events = pd.read_json(f"../open-data/data/events/{game_id}.json") df_three_sixty = pd.read_json(f"../open-data/data/three-sixty/{game_id}.json")The next step involves linking the 360 Data with the Events data. StatsBomb's coordinate system is set up so that a player's team always attacks towards the right side of the field, irrespective of whether they are playing at home or away (with certain exceptions based on play type). This setup presents a challenge: when offensive and defensive roles switch, the player's coordinates are effectively flipped. Therefore, to maintain clarity and consistency according to the SPADL format, it is crucial that the team's attacking direction remains fixed and easily viewable.
import math def convert_xy(x, y, inversion=False): x = min(120, max(1, x if x else 1)) y = min(80, max(1, y if y else 1)) x = ((x - 1) / 119) * 105.0 y = 68 - ((y - 1) / 79) * 68.0 if inversion: x = 105.0 - x y = 68.0 - y return x, y def judge_home_team_player(freeze_frame, is_home_team): if is_home_team: if freeze_frame['teammate'] is True: return True else: return False else: if freeze_frame['teammate'] is True: return False else: return True def select_color(freeze_frame, is_home_team, is_home_team_player=None): if is_home_team_player is None: is_home_team_player = judge_home_team_player(freeze_frame, is_home_team) if is_home_team_player: if freeze_frame['actor'] is True: return "red" elif freeze_frame['keeper'] is True: return "darkorange" else: return "orange" else: if freeze_frame['actor'] is True: return "blue" elif freeze_frame['keeper'] is True: return "darkblue" else: return "lightblue" def judge_home_team(home_team_id, team_id, df_events, _event_id): is_home_team = (team_id == home_team_id) ball_receipt = df_events[df_events["id"] == _event_id]["ball_receipt"].tolist()[0] if isinstance(ball_receipt, dict) and ball_receipt['outcome']['id'] == 9: # Incomplete is_home_team = not is_home_team dribble = df_events[df_events["id"] == _event_id]["dribble"].tolist()[0] if isinstance(dribble, dict) and dribble['outcome']['id'] == 9: # Incomplete is_home_team = not is_home_team return is_home_team def judge_foul(df_events, _event_id): event_type = df_events[df_events["id"] == _event_id]["type"].tolist()[0] if isinstance(event_type, dict) and event_type['id'] == 21: # Foul Won return True else: return Falselocation_data = [] for index, row in df_events.iterrows(): event_id = row["id"] event_timestamp = row["timestamp"] related_event_ids = row["related_events"] idx = row["index"] team_id = row["team"]['id'] is_home_team = judge_home_team(home_team_id, team_id, df_events, event_id) is_foul = judge_foul(df_events, event_id) inversion = is_home_team if is_foul else not is_home_team obj_freeze_frames = df_three_sixty[df_three_sixty["event_uuid"] == event_id]["freeze_frame"] obj_visible_area = df_three_sixty[df_three_sixty["event_uuid"] == event_id]["visible_area"] if len(obj_freeze_frames) == 0: continue if not isinstance(row["player"], dict) or "id" not in row["player"]: continue player_id = row["player"]["id"] for freeze_frames in obj_freeze_frames: for ff in freeze_frames: is_home_team_player = judge_home_team_player(ff, is_home_team) pid = player_id if ff["actor"] is True else None locations.append({"teammate": ff["teammate"], "actor": ff["actor"], "keeper": ff["keeper"], "location": ff["location"], "xy": [x, y], "home_team_player": is_home_team_player, "player_id": pid, 'player_id_expected': None}) location_data.append({"event_id": event_id, "locations": locations, "visible_area": visible_area, "timestamp": event_timestamp})First, the per-frame data stored in location_data is used to compute the distance between each pair of coordinates (representing players) observed in each individual frame. Additionally, for cases where a player steps outside the Visible Area (the screen boundary), the calculation also includes determining the distance to the nearest line segment that defines this boundary.
from scipy.spatial import distance from numpy.linalg import norm FIELD_SIZE_X, FIELD_SIZE_Y = 105.0, 68.0 FIELD_SIZE_XY = math.sqrt(pow(FIELD_SIZE_X, 2) + pow(FIELD_SIZE_Y, 2)) def split_locations_home_away(locations): locations_home, locations_home_keeper, locations_away, locations_away_keeper = [], [], [], [] for location in locations: if location["home_team_player"]: if location["keeper"]: locations_home_keeper.append(location) else: locations_home.append(location) else: if location["keeper"]: locations_away_keeper.append(location) else: locations_away.append(location) return locations_home, locations_home_keeper, locations_away, locations_away_keeper def calc_distance_matrix(locations_1, locations_2): xys_1 = [location["xy"] for location in locations_1] xys_2 = [location["xy"] for location in locations_2] return distance.cdist(xys_1, xys_2, metric='euclidean') def calc_distance_and_neighbor_point(a, b, p): ap = p - a ab = b - a ba = a - b bp = p - b if np.dot(ap, ab) < 0: distance = norm(ap) neighbor_point = a elif np.dot(bp, ba) < 0: distance = norm(p - b) neighbor_point = b else: ai_norm = np.dot(ap, ab)/norm(ab) neighbor_point = a + (ab)/norm(ab)*ai_norm distance = norm(p - neighbor_point) return (neighbor_point, distance) def is_touchline(va1, va2): if va1[0] == 0.0 and va2[0] == 0.0: return True elif va1[0] == FIELD_SIZE_X and va2[0] == FIELD_SIZE_X: return True elif va1[1] == 0.0 and va2[1] == 0.0: return True elif va1[1] == FIELD_SIZE_Y and va2[1] == FIELD_SIZE_Y: return True else: return False def _calc_distances_location_visible_area(xys, visible_area): distances = np.zeros((len(xys), 1), dtype=np.float64) for i, xy in enumerate(xys): _distances = np.zeros((len(visible_area) - 1, 1), dtype=np.float64) for j in range(0, len(visible_area)-1): va1, va2 = visible_area[j], visible_area[j+1] if is_touchline(va1, va2): d = FIELD_SIZE_XY else: _, d = calc_distance_and_neighbor_point(np.asarray(va1), np.asarray(va2), np.asarray(xy)) _distances[j, 0] = d distances[i, 0] = np.min(_distances) return distances def calc_distances_location_visible_area(locations, visible_area): xys = [location["xy"] for location in locations] return _calc_distances_location_visible_area(xys, visible_area) def calc_distances_location_visible_area_xy(locations, visible_area_x, visible_area_y): xys = [location["xy"] for location in locations] distances = np.zeros((len(xys), 2), dtype=np.float64) distances[:, 0:1] = _calc_distances_location_visible_area(xys, visible_area_x) distances[:, 1:2] = _calc_distances_location_visible_area(xys, visible_area_y) return distances.max(axis=1).reshape((len(xys), 1)) def make_lp_matrix(xy_distance_matrix, x_distances, y_distances): lp_matrix = np.zeros((x_distances.shape[0]+1, y_distances.shape[0]+1), dtype=np.float64) lp_matrix[0:x_distances.shape[0], 0:y_distances.shape[0]] = xy_distance_matrix lp_matrix[0:x_distances.shape[0], y_distances.shape[0]:y_distances.shape[0]+1] = x_distances lp_matrix[y_distances.shape[0]:y_distances.shape[0]+1, 0:y_distances.shape[0]] = y_distances.T return lp_matrix def filter_unreal_distance(lp_matrix, s, meter_per_second=10.0, s_noise=0.5): return np.where(lp_matrix > meter_per_second * (s + s_noise), FIELD_SIZE_XY, lp_matrix)Mapping points by analyzing the distances between them across frames can be approached as a linear programming transportation issue. Essentially, this involves determining the most efficient way to move goods from supply locations (warehouses) to meet needs at various demand points (projects). For those interested in tackling this problem using Python, detailed instructions are available here: https://machinelearninggeek.com/solving-transportation-problem-using-linear-programming-in-python/.
When tackling the point-to-point correspondence issue, we treat the coordinates in the initial frame as supply locations and those in the subsequent frame as demand locations. This transforms our task into one of moving goods with a unit quantity from each supply spot to each demand spot. The cost involved is determined by the distance between these points. In this context, off-screen areas are considered a single region, and transitioning between on-screen coordinates and off-screen spaces equates to traveling across distances defined by the boundaries of the Visible Area.
from pulp import * from collections import defaultdict def make_assignments(lp_matrix, verbose=False, number_of_assignments=10): num_x, num_y = lp_matrix.shape assignments_y_x, assignments_x_y = {}, {} warehouses = [str(i) for i in range(num_x)] projects = [str(i) for i in range(num_y)] supply = {str(i): 1 if i != (num_x - 1) else (number_of_assignments + 1 - num_x) for i in range(num_x)} demand = {str(i): 1 if i != (num_y - 1) else (number_of_assignments + 1 - num_y) for i in range(num_y)} costs = lp_matrix costs = makeDict([warehouses, projects], costs, 0) prob = LpProblem("Material_supply_Problem", LpMinimize) Routes = [(w, b) for w in warehouses for b in projects] vars = LpVariable.dicts("Route", (warehouses, projects), 0, None, LpInteger) prob += ( lpSum([vars[w][b] * costs[w][b] for (w, b) in Routes]), "Sum_of_Transporting_Costs", ) for w in warehouses: prob += ( lpSum([vars[w][b] for b in projects]) == supply[w], "Sum_of_Products_out_of_warehouses_%s" % w, ) for b in projects: prob += ( lpSum([vars[w][b] for w in warehouses]) <= demand[b], "Sum_of_Products_into_projects_%s" % b, ) for w in warehouses: for b in projects: if w == str(num_x - 1) and b == str(num_y - 1): continue prob += ( vars[w][b] <= 1, "Sum_of_Products_on_routes_%s_%s" % (w, b), ) prob.solve() if verbose: print("Status =", LpStatus[prob.status]) for v in prob.variables(): if v.varValue > 0: _, x_idx, y_idx = v.name.split("_") assignments_y_x[y_idx] = {x_idx: {"value": v.varValue}} assignments_x_y[x_idx] = {y_idx: {"value": v.varValue}} if verbose: print("Value of Objective Function =", value(prob.objective)) return assignments_y_x, assignments_x_y, value(prob.objective)import datetime def get_distance(assignments_y_x, lp_matrix): for y in assignments_y_x: for x in assignments_y_x[y]: assignments_y_x[y][x]["distance"] = lp_matrix[int(x)][int(y)] return assignments_y_x list_average_vof_home, list_assignments_y_x_home, list_assignments_x_y_home = [], [], [] list_average_vof_away, list_assignments_y_x_away, list_assignments_x_y_away = [], [], [] for i in range(0, len(location_data)-1): ldx = location_data[i] ldy = location_data[i+1] lcx_home, lcx_home_keeper, lcx_away, lcx_away_keeper = split_locations_home_away(ldx["locations"]) lcy_home, lcy_home_keeper, lcy_away, lcy_away_keeper = split_locations_home_away(ldy["locations"]) diff = float((ldy["timestamp"] - ldx["timestamp"]).total_seconds()) # home team if len(lcx_home) == 0 or len(lcy_home) == 0: list_assignments_y_x_home.append({}) list_assignments_x_y_home.append({}) list_average_vof_home.append(FIELD_SIZE_XY) else: xy_distance_matrix_home = calc_distance_matrix(lcx_home, lcy_home) x_distances_home = calc_distances_location_visible_area_xy(lcx_home, ldx["visible_area"], ldy["visible_area"]) y_distances_home = calc_distances_location_visible_area_xy(lcy_home, ldx["visible_area"], ldy["visible_area"]) lp_matrix_home = make_lp_matrix(xy_distance_matrix_home, x_distances_home, y_distances_home) lp_matrix_home = filter_unreal_distance(lp_matrix_home, diff) assignments_y_x_home, assignments_x_y_home, vof = make_assignments(lp_matrix_home, verbose=False) assignments_y_x_home = get_distance(assignments_y_x_home, lp_matrix_home) assignments_x_y_home = get_distance(assignments_x_y_home, lp_matrix_home.T) list_assignments_y_x_home.append(assignments_y_x_home) list_assignments_x_y_home.append(assignments_x_y_home) list_average_vof_home.append(vof / len(assignments_x_y_home)) # away team if len(lcx_away) == 0 or len(lcy_away) == 0: list_assignments_y_x_away.append({}) list_assignments_x_y_away.append({}) list_average_vof_away.append(FIELD_SIZE_XY) else: xy_distance_matrix_away = calc_distance_matrix(lcx_away, lcy_away) x_distances_away = calc_distances_location_visible_area_xy(lcx_away, ldx["visible_area"], ldy["visible_area"]) y_distances_away = calc_distances_location_visible_area_xy(lcy_away, ldx["visible_area"], ldy["visible_area"]) lp_matrix_away = make_lp_matrix(xy_distance_matrix_away, x_distances_away, y_distances_away) lp_matrix_away = filter_unreal_distance(lp_matrix_away, diff) assignments_y_x_away, assignments_x_y_away, vof = make_assignments(lp_matrix_away, verbose=False) assignments_y_x_away = get_distance(assignments_y_x_away, lp_matrix_away) assignments_x_y_away = get_distance(assignments_x_y_away, lp_matrix_away.T) list_assignments_y_x_away.append(assignments_y_x_away) list_assignments_x_y_away.append(assignments_x_y_away) list_average_vof_away.append(vof / len(assignments_x_y_away))Accurate Player Identification for Comprehensive Game Analysis
The accurate identification of player movements is a crucial aspect in the analysis of game dynamics. By utilizing an LP (Linear Programming) solution, we can predict Player IDs based on assignments derived from the model's output. This method ensures that if a Player ID is already established at a specific point within the reference frame, this identifier will be maintained for future predictions at that location. Conversely, when a predicted Player ID exists within the reference frame but lacks a corresponding definition, it will be assigned to the nearest relevant point unless it has already been allocated elsewhere.This approach offers precision by maintaining consistency in player identification across various frames, allowing for seamless tracking and analysis. Such meticulous attention to detail enhances our understanding of player behavior over time, offering deeper insights into their strategies and performance metrics during gameplay. The underlying logic embedded in the code facilitates this process efficiently, ensuring that each player's identity remains coherent throughout different phases of motion capture and data analysis.
def get_player_id_expected(locations_y, locations_x, assignments_y_x, is_reverse=False): player_id_y = list(filter(None, [x['player_id'] for x in locations_y])) player_id_y = player_id_y[0] if len(player_id_y) != 0 else -1 for j in range(len(locations_y)): if str(j) in assignments_y_x and len(assignments_y_x[str(j)]) == 1: for i in range(len(locations_x)): if str(i) in assignments_y_x[str(j)]: player_id_x = locations_x[i]['player_id'] player_id_expected_x = locations_x[i]['player_id_expected'] if not is_reverse: if player_id_x is not None: # If player_id exists at the reference coordinate, it is assumed to be player_id_expected. locations_y[j]['player_id_expected'] = player_id_x elif player_id_expected_x is not None: if player_id_expected_x != player_id_y: # If player_id_expected of the reference coordinate does not match player_id of the frame in question, it is assumed to be player_id_expected as is. locations_y[j]['player_id_expected'] = player_id_expected_x else: # If player_id_expected of the reference coordinate matches player_id of the frame in question if locations_y[j]['player_id'] == player_id_y: # If it is the player_id of the coordinate in question, then player_id_expected locations_y[j]['player_id_expected'] = player_id_expected_x else: # Otherwise, it should be at a different coordinate in the frame in question, so player_id_expected at that coordinate is set to None. locations_y[j]['player_id_expected'] = None else: pass # player_id_expected_x is None else: if player_id_x is not None: # If player_id exists at the reference coordinate, it is assumed to be player_id_expected. locations_y[j]['player_id_expected'] = player_id_x elif player_id_expected_x is not None: if player_id_expected_x != player_id_y: # If player_id_expected of the reference coordinate does not match player_id of the frame in question, it is assumed to be player_id_expected as is. locations_y[j]['player_id_expected'] = player_id_expected_x else: # In the case of reverse order, the player_id_expected of the reference coordinate is estimated from the player_id of the frame in question, # it should not be reverse-estimated as the player_id_expected of the coordinate in question return locations_y def get_number_of_collect_player_id(locations): num_collect, num_incollect = 0, 0 player_id = list(filter(None, [x['player_id'] for x in locations])) if len(player_id) == 0: return num_collect, num_incollect else: player_id = player_id[0] for location in locations: if location['player_id_expected'] == player_id: # not None if location['player_id'] == player_id: num_collect += 1 else: num_incollect += 1 return num_collect, num_incollect def get_number_of_player_id_expected(locations): return len(list(filter(None, [x['player_id_expected'] for x in locations])))N = len(_location_data) - 1 average_vof_away_threshold = 10.0 _location_data = copy.deepcopy(location_data) for i in range(0, N): # Expect forward ldx = _location_data[i] ldy = _location_data[i+1] lcx_home, lcx_home_keeper, lcx_away, lcx_away_keeper = split_locations_home_away(ldx["locations"]) lcy_home, lcy_home_keeper, lcy_away, lcy_away_keeper = split_locations_home_away(ldy["locations"]) assignments_y_x_home = list_assignments_y_x_home[i] assignments_y_x_away = list_assignments_y_x_away[i] if list_average_vof_home[i] > average_vof_away_threshold or list_average_vof_away[i] > average_vof_away_threshold: continue lcy_home = get_player_id_expected(lcy_home, lcx_home, assignments_y_x_home) lcy_away = get_player_id_expected(lcy_away, lcx_away, assignments_y_x_away) _location_data[i+1]["locations"] = lcy_home + lcy_home_keeper + lcy_away + lcy_away_keeper for i in range(N-1, -1, -1): # Expect backward ldx = _location_data[i] ldy = _location_data[i+1] lcx_home, lcx_home_keeper, lcx_away, lcx_away_keeper = split_locations_home_away(ldx["locations"]) lcy_home, lcy_home_keeper, lcy_away, lcy_away_keeper = split_locations_home_away(ldy["locations"]) assignments_x_y_home = list_assignments_x_y_home[i] assignments_x_y_away = list_assignments_x_y_away[i] if list_average_vof_home[i] > average_vof_away_threshold or list_average_vof_away[i] > average_vof_away_threshold: continue lcx_home = get_player_id_expected(lcx_home, lcy_home, assignments_x_y_home, is_reverse=True) lcx_away = get_player_id_expected(lcx_away, lcy_away, assignments_x_y_away, is_reverse=True) _location_data[i]["locations"] = lcx_home + lcx_home_keeper + lcx_away + lcx_away_keeperThe precision of the Player ID, as previously outlined, has been quantitatively assessed. By transmitting the known Player ID to both preceding and succeeding frames, we can gauge this prediction method's accuracy. This is done by evaluating the consistency between the actual Player ID and its predicted counterpart at the propagation target.}
{As delineated earlier, we have systematically evaluated the accuracy of our estimated Player IDs. The process involves extending a verified Player ID to adjacent frames—both before and after—to measure how well our predictions align with reality. We quantify this by comparing the true Player IDs with those predicted at these propagated points.
Optimizing Point-to-Point Prediction Accuracy and Player ID Occupancy Efficiency
In the realm of sports analytics, accurately tracking and predicting player movements is paramount for strategizing and performance analysis. The ability to predict Player IDs with precision directly impacts the quality of insights derived from game footage. An essential aspect of this process is **Point-to-Point Player ID Prediction Accuracy**. This accuracy hinges on how similar or connected consecutive frames are; when frames show a high degree of similarity, prediction propagation across these frames maintains a high accuracy level. Conversely, forcing predictions across dissimilar frames results in reduced accuracy at the destination frame due to inconsistent player movements. Therefore, achieving an optimal trade-off between propagation distance and prediction accuracy is crucial for maintaining overall efficiency.Another vital factor in this domain is **Player ID Occupancy Efficiency**. This metric evaluates how effectively predicted Player IDs match actual tracked points relative to the total number of possible points that could be tracked. A strategy that overly restricts prediction propagation might preserve high accuracy but at the expense of lower occupancy efficiency—meaning fewer player IDs are predicted than potentially could be. Hence, balancing between maintaining high prediction accuracy and maximizing occupancy efficiency ensures the player ID prediction system operates at peak effectiveness.
In summary, optimizing both point-to-point prediction accuracy and occupant efficiency forms the backbone of an effective player tracking system in sports analytics. Striking a balance between these two aspects not only enhances predictive reliability but also ensures comprehensive tracking which can significantly augment strategic planning and performance assessments during games.
import statistics sum_collect, sum_incollect = 0, 0 occupancies = [] for i in range(0, N): ldx = _location_data[i] lcx_home, lcx_home_keeper, lcx_away, lcx_away_keeper = split_locations_home_away(ldx["locations"]) num_collect_home, num_incollect_home = get_number_of_collect_player_id(lcx_home) num_player_id_expected_home = get_number_of_player_id_expected(lcx_home) num_collect_away, num_incollect_away = get_number_of_collect_player_id(lcx_away) num_player_id_expected_away = get_number_of_player_id_expected(lcx_away) sum_collect += num_collect_home + num_collect_away sum_incollect += num_incollect_home + num_incollect_away if len(lcx_home) != 0: occupancies.append(num_player_id_expected_home / len(lcx_home)) if len(lcx_away) != 0: occupancies.append(num_player_id_expected_away / len(lcx_away)) print(average_vof_away_threshold, sum_collect, sum_incollect, sum_collect / (sum_collect + sum_incollect), statistics.mean(occupancies))Player Tracking Systems: Striking the Balance Between Accuracy and Identification Rate
The findings suggest a trade-off between estimation accuracy and the rate at which players can be identified. By adjusting the threshold for the average distance moved by a player between frames, organizations can optimize player tracking systems based on their specific performance requirements. This research provides valuable insights for organizations developing and deploying sports analytics solutions, as it highlights the importance of carefully considering the trade-offs between accuracy and estimation rate when designing player tracking systems.Understanding these dynamics allows organizations to tailor their systems more effectively to meet various strategic goals. For instance, if real-time decision-making is crucial, then prioritizing a higher identification rate may be beneficial despite potential compromises in accuracy. Conversely, scenarios demanding precise performance metrics might warrant stricter thresholds to ensure accurate data capture even if it slows down identification processes.
Ultimately, striking an optimal balance hinges on specific application needs, making it essential for developers to fine-tune parameters like movement thresholds. Such adjustments enable them to align technological capabilities with organizational objectives, thereby maximizing both efficiency and effectiveness in sports analytics implementations.
The estimation rate failed to reach 100% primarily because when several players exited the visible area during a football relay, it became challenging to identify which players were re-entering. Additionally, the overhead camera view was sometimes interrupted due to zooming in on specific players, causing discontinuity in the footage. Nonetheless, since the positions of players near the ball can be estimated with greater accuracy, it is feasible to focus on key areas for game analysis.
The following figure illustrates how a Player ID can be estimated using this method. It presents the coordinates of a player captured during a frame from the match between Bayer Leverkusen and RB Leipzig, which took place on August 19, 2023. This game is identified in StatsBomb OpenData as game_id 3895052.
Initially, we showcase the data that can be extracted solely from 360 Data. While it is beneficial to determine the positions of players from both the home and away teams, without estimating Player IDs, only one out of the 19 field players visible in that area can be precisely identified based on their position.
To illustrate the effectiveness of our player estimation method, we present the results achieved. Using this approach, we successfully identified and assigned names to 15 out of 19 players visible on the screen with a remarkable 97% accuracy rate. Such an enhancement in data allows for a more profound analysis, including precise quantification of player positioning.}
{We demonstrated our player estimation technique by mapping out its outcomes. Impressively, our method enabled us to accurately assign names to 15 of the 19 players depicted on-screen, achieving an accuracy rate of 97%. This advancement not only boosts information clarity but also paves the way for deeper analytical insights like detailed assessments of player positions.}
{The results from applying our player estimation strategy are now showcased. With this methodology, we managed to correctly identify and label 15 out of the 19 players seen on screen with an impressive accuracy level of 97%. We believe that such enhancements in information can facilitate more comprehensive analyses, including accurate measurements of where each player is positioned.
We have now made available on GitHub the estimated Player IDs for Bayer Leverkusen's matches in the Bundesliga 2023/2024 season. This dataset, compiled using the methodology outlined in our article, aims to aid game analysis and provide valuable insights.}
{For those interested in further data processing or code organization, I am open to fulfilling such requests. You can find the repository here: https://github.com/KazumaMurao/open-data/tree/master/data/three-sixty-with-player-id
References
Introduction to Football Analytics - StatsBomb | Data Champions
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Source： DriblabData Analytics in Football
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Source： footballyticsFootball Analytics with Python & R: Learning Data Science Through ...
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Source： Amazon.comData & Analytics
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Source： Knowable MagazineFootball Analytics
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Source： KaggleAnalytics FC | Home
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Source： Analytics FC
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