随着精英职业足球的表现分析过程的成熟, 为领先俱乐部工作的分析师们正努力通过多个数据集来识别战术上的相关见解和趋势, which in many cases are utilised in isolation of one another.
One such dataset has been domestic tracking data, available to all clubs across a league, which captures the positioning and movement of all 22 players on the field. This data, which has been captured for over 20 years, allows analysts to review the actions of every player against set KPIs, from both a performance and tactical perspective. Historically this has only been available in a raw format, 因此，在确定长期趋势方面存在局限性.
However in recent months a significant number of metrics, derived from tracking data, have been made available in a merged tracking and event dataset, 哪些是第一次可以在前端分析工具中进行询问的, Stats Perform’s ProVision. 这为绩效分析开辟了新的途径，以提供更客观的见解, in a matter of seconds, into strengths and weaknesses of opposition players.
In total, 与跟踪相关的40多种不同的过滤器可以与事件数据一起使用, which cover the following three areas:
- Player location data: featuring average distance metrics and off the ball runs.
- Performance metrics: including extensive player distance and sprint data.
- Passing data: 包括传球速度，总传球次数和详细的传球数据.
Using this information it is now possible to build reports, run on a daily or weekly basis, which provide another layer of context to event outputs.
1 – Good and Bad Decision-Making From Crosses
合并数据集的一个关键好处是，对于发生的每一个球事件, we have an understanding of the location of every player on the pitch. 这对于分析边路球员的交叉表现特别有用.
One of the tracking filters in ProVision enables a user to determine 传球时，有多少球员在对方的禁区内. 这使得分析人员可以在禁区内的队友很少的情况下找出那些经常传中的球员, which can highlight bad decision-making in attacking areas.
Conversely, 使用相同的原则，分析师也可以使用这个过滤器来突出那些在球在大范围内时想要包住对方禁区的球队, looking to get on the end of deliveries played in. This can be analysed on a play-by-play level, at game level and season level, 平均数字提供了一个洞察球队的趋势，从开放的传中, compared to competition averages, from both sides of the pitch.
So as well as identifying the number of crosses and their outcome, vip威尼斯登录入口现在有了额外的背景，以帮助了解这些结果背后的关键原因.
2 – Progressive Passing By Central Defenders
When analysing the passing tendencies of defenders, 准确地判断哪些球员能够有效地将球带出防守，并成功地将球带出场地，可以影响球队在无球情况下的进攻.
Filtering from event data, such as the direction, start/end location and outcome of passes, can offer insights into how comfortable a defender is in possession. However by adding tracking, we can take this analysis further.
Using this data, an analyst can apply a filter for how many opposition players are bypassed by a pass, as well as the average number of players who are bypassed per pass. Other filters can also be applied to fit a club’s own definition, 包括缩小传球的数量，只包括地面传球和传球的场地.g the defensive third).
Using these insights, across multiple matches or against specific types of opposition, 分析人员可以识别出得分较高的对手球员，并将他们的表现与其他传球输出进行比较, 哪一种方法可以帮助vip威尼斯登录入口识别出那些擅长传球的中后卫呢 Possession Value (PV), 也要确定哪些球员可以通过递进传球增加球队得分的概率. 它还可以帮助识别那些控球效率不高的后卫，以及那些不太适应被压制的后卫, which leads in nicely to our next example.
3 – Passing Performance Under Pressure
追踪数据的一个关键好处是，分析师可以确定在场上的每个球员在任何时候离控球球员有多近. This means that each player’s passing performance can be analysed, 考虑到当他们有对手球员近距离接触时他们的表现.
ProVision allows an analyst to add a tracking filter to passing information, where they can see how their numbers fluctuate based on how many opposition players are within two metres of the ball. If a player has at least two defensive players in close proximity, 当他们试图传球时，他们可能会承受更大的压力——这些数据可以用来比较一个球队中球员的表现, 当他们面临更大的压力时，谁最有可能丢球.
4 – Linking Work Rate to Effective Pressing
When working solely with event data, the PPDA metric (passes per defensive action), used in conjunction with high turnovers, has been a useful proxy to identify if a team looks press the opposition. A low PPDA total, across multiple matches, would indicate that a team strives to win the ball back quickly, 而一个高的总得分意味着一个球队更有可能坐在一个低的街区，让对手有球.
When we add physical tracking data, 从事件数据中得出的见解与每个运动员在场上的运动强度之间建立相关性是可能的. We can do this using two metrics available within the merged dataset: peak player speeds, which is measured in metres covered per second; and the total metres covered by each player at high speed.
同时也提供了一些背景信息，比如哪些球员在采用侵略性紧逼战术的球队中占据了最多的位置, 这些输出可以应用于各种ProVison过滤器，以确定球员的工作效率和球队在面对不同类型对手时的控球方法如何变化, such as teams who look to play over the top of a press, compared to their season averages.
5 – Use of Personnel at Attacking Corners
During 2020/21, 在欧洲五大联赛中，20%的非点球进球来自定位球, 这个数字强调了球队最大限度地利用角球和任意球机会的重要性, as well as being able to defend them successfully.
Goals Scored From Corners: 2020-21 Season
Whilst advanced metrics, such as Expected Goals, 哪些球队在角球中创造和交出了高质量的机会, 玩家位置数据的可用性为团队在这些情况下的设置增加了另一个维度.
Using tracking filters, 分析人员可以确定一个赛季中一个团队设置了一个 specific number of players in the penalty area, or inside the six-yard box. So for example, 一个俱乐部可以识别出防守方有多少次在罚球时必须在6码范围内对付两个进攻方球员，关键的是他们在这些情况下有多少机会失手.
这可以帮助球队在防守定位球时发现潜在的弱点, across multiple matches, which a team can use to inform their tactics in the lead-up to a game.
These are just five examples of how a merged domestic dataset, combining outputs from tracking and event data, can help with the identification of players who either excel, or potentially have an area of weakness, during recurring in-game scenarios.
了解ProVision如何对长期绩效趋势提供更深入的见解, supporting your club’s pre and post-match processes, visit our Match Analysis page or contact us to find out if tracking data, from your domestic league, has been integrated into the platform.