Atalanta has been one of the great sensations from this season. From achieving 3rd place in the Serie A to playing in the Champions League. A lot has been spoken about Gasperini and his players.
In this post we will be looking at their performance in accordance with the study of Liu (2015) and Gómez, Lago-Peñas, C., and Pollard, R. (2013) on the influence and impact of performance and situational variables on the teams’ performance, in this particular study case we will be looking into matches against teams in a higher ranking and lower ranking from Atalanta in the Serie A and also performance per half.
In accordance with the UEFA Rankings the matches against the following teams were chosen to be analysed.
High Ranking: Juventus, Roma, Lazio and Napoli
Low Ranking: Parma, Fiorentina, Sassuolo and Torino
Liu (2015) quotes in his study that performance of football teams is varying constantly due to the dynamic nature of the sport, whilst the typical performance and its spread can be represented by profiles combining different performance-related variables based on data from multiple matches.
In sports performance analysis, profiles can be defined as combinations and collections of reliable and valid performance indicators bringing together to show the performance of an athlete and/or a team (O’Donoghue, 2013).
Hughes et al (2001) and O’Donoghue (2005 and 2013) mention that performance profiles can represent teams’ typical performance and its spread by using performance-related variables basing it on data from multiple matches.
We looked in a total of 8 matches, 4 vs High Ranking teams and 4 vs Low Ranking teams.
It has been found that both technical-tactical and physical performances of football can be influenced situational conditions in a behavioural level (Gómez et al., 2013; Lago-Peñas, 2012; Taylor et al., 2010). Examples for situational conditions are Match location (home/away), match outcome (winning/win, losing/loss drawing/draw), type of competition (league, cup, international challenge, final of a competition), strength of the team and opponent and so on (Gómez et al., 2013; Lago-Peñas, 2012; Mackenzie and Cushion, 2013; Sarmento et al., 2014; Taylor et al., 2010; Taylor et al., 2008).
Atalanta shows tactical consistency by always performing in a 1-3-4-2-1 on all matches. 60.62% of their touches were on the central corridor, showing that they predominately play in the central corridor and 46.33% of their touches were in the midfield third, this is due to their possession style. Note for the amazing goal average of 3.25 goals per game and 17.88 shots per game.
Knowing that Atalanta is also a team that concedes goals as well, data showed that they concede 1.38 goals per game. Atalanta is well known for their high press and man to man marking style of press, this method is generally used as soon Atalanta loses possession, especially if the ball inside the opposition’s defensive third, this allows Atalanta to press high, keeping a high defensive line and recover the ball to quickly counter-attack taking advantage of the opposition’s disorganised positioning due to being attacking, however, overall, they still do more interceptions than recovering the ball from tackles, with an average of 42.25 interceptions per game and 15.5 balls recovered from tackles per game.
On the average table we can highlight Atalanta’s attacking power, with an average of 17.88 shots per game, 8.38 shots on target per game and 3.25 goals per game, a remarkable stat for this team. One of the styles of this Atalanta happens inside the final third, a very common movement is the use of penetration passes to break lines and penetrate inside the 18-yard box from the half-spaces, data showed an average of 58.38 penetration passes made per game.
In 8 matches, won 5, drew 2 and lost 1. What we can straight away see is against the Low Ranking teams they won all matches and against the High Ranking teams they only won once. We will look more in depth now on what could be the reason Atalanta had better results when played against Low Ranking Teams and only one win against High Ranking teams.
Comparing Atalanta’s performances on both halves and against both High and Low Ranking teams we can start seeing a general decline on several KPI’s on from 1st half performance to 2nd half performance.
Looking on 1st half performance as excepted against Low Ranking teams performance was better, however, we can see some changes in some of the performance indicators, Atalanta vs High Ranking teams had more short passes completed, long passes intercepted, balls recovered by interceptions and more goals conceded.
With an average of 2.5 goals scored and 0.25 goals conceded per 1st half and 3 out of 4 winning results by the end of the 1st half against Low Ranking teams, Atalanta enters their 2nd halves more in game management mindset decreasing all in general almost all performance indicators.
With an average of 1 goal scored and 1.75 goals conceded per 1st half and 1 out 4 winning results by the end of the 1sthalf against High Ranking teams, Atalanta enters their 2nd halves with a more consistent level of performance similar to the 1st half as shown on the radar charts and averages table, with only slight decreases of performance in some performance indicators.
Overall Atalanta between both performances maintains a team playing more on the central corridor 60.41% vs High Ranking / 60.77% vs Low Ranking. Data showed an interesting scenario influencing the performance of the team.
When playing in wide areas data shows the Left Corridor as the preferred corridor to be used vs High Ranking teams (21.43% over 18.16%), overall difference of 3.27% and the Right Corridor as the preferred corridor to be used vs Low Ranking teams (21.19% over 18.04%), overall difference of 3.15%.
Looking again on the overall performance on wide areas we saw previously that both Left and Right Corridors had similar values 19.68% (left) and 19.70% (right), so what did cause the discrepancy?
The answer was found in one player, Josip Ilicic.
Ilicic is one of the key players of this squad.
During the matches against the High Ranking teams Ilicic only started in 2 of them, was unavailable from 1 and came out of the bench mid 2nd half on the other.
As shown on data Ilicic this season so far as scored 21 goals and has an 82.04% pass success rate rate and 59.26% dribbling success rate. Not having him available on the right side made Atalanta change their focus to the left side where we can find the maestro of this team, Alejandro “Papu” Gomez.
Data showed that Gomez is responsible for 115 key passes, with a passing success rate of 85.30% and a dribbling success rate of 58.88%.
Gasperini has given him the freedom to conduct this team, Gomez normally plays in both left and central corridor confirming the increase of the activity on the left corridor against High Ranking teams.
From what data showed, we can conclude Atalanta performs better on the 1st half. We can see as well that the result at the end of the 1st half is a variable that has to be considered always, it will dictate the behaviour of the team for the 2nd half, it has been demonstrated that football players perform less high-intensity activity when winning than when losing (Bloomfield et al., 2005b; Castellano et al., 2011; Lago et al., 2010; Shaw and O’Donoghue, 2004), also to consider is the absence of either Gomez and Ilicic and the impact of which wide areas will be used more during the match.
Liu (2015) concludes, results showed that High Ranking teams’ home and away differences generally appeared in variables relating to scoring and defending, however, significant differences were observed in most variables for Low Ranking teams. This also confirmed the conclusion of Lago-Peñas and Lago-Ballesteros (2011), indicating “teams described as High and Low, would not experience the same home advantage, which tells that High Ranking technical and tactical performance is more stable regardless if playing either home or away than Low Ranking teams.
Session 1
Focus: Key Penetration Passes in the Final Third
Small Sided Game
4v4 + 1 (Magic Man) + 2 (Wide Players) + GK
Team attacking the goal has the challenge to pass the ball into one of the red zones and a player receiving it inside one of them, if player successfully receives the ball in the red zone and scores from there the goal will count as 2. Attacking team can score in normal conditions, however it will only count as 1.
Defending team to recover the ball as quick as possible applying high pressure and score in one of the mini goals.
Session 2
Focus: High Pressing in Opposition Half
Small Sided Game
9v9
After a goal, game restarts from goal kick from the side that conceded the goal.
Challenge: team to recover the ball always in the opposition half.
Condition to score: Team can only score if all outfield players from the team are inside the opposition half, including halfway line.
References
- Almeida, C. H., Ferreira, A. P., and Volossovitch, A. (2014), Effects of Match Location, Match Status and Quality of Opposition on Regaining Possession in UEFA Champions League, Journal of Human Kinetics, 41, 203-214.
- Bloomfield, J.R., Polman, R.C.J. and O’Donoghue, P.G. (2005b) ‘Effects of score-line on team strategies in FA Premier League Soccer’, Journal of Sports Sciences, 23: 192–3.
- Castellano, J., Blanco-Villaseñor, A. and Álvarez, D. (2011) ‘Contextual variables and time motion analysis in soccer’, International Journal of Sports Medicine, 32: 415–21.
- Gómez, M. A., Lago-Peñas, C., and Pollard, R. (2013). Situational Variables. In T. McGarry, P. O’Donoghue & J. Sampaio (Eds.), Routledge Handbook of Sports Performance Analysis (pp. 259-269). London: Routledge.
- Gómez, M. A., Lago-Peñas, C., and Pollard, R. (2013). Situational Variables. In T. McGarry, P. O’Donoghue & J. Sampaio (Eds.), Routledge Handbook of Sports Performance Analysis (pp. 259-269). London: Routledge.
- Hongyou Liu, Qing Yi, Jesús-Vicente Giménez, Miguel-Angel Gómez & Carlos Lago-Peñas (2015) Performance profiles of football teams in the UEFA Champions League considering situational efficiency, International Journal of Performance Analysis in Sport, 15:1, 371-390
- Hughes, M. D., Evans, S., and Wells, J. (2001), Establishing normative profiles in performance analysis, International Journal of Performance Analysis in Sport, 1, 1-26.
- Lago-Peñas, C. (2012), The role of situational variables in analysing physical performance in soccer, Journal of Human Kinetics, 35, 89-95.
- Lago-Peñas, C., and Lago-Ballesteros, J. (2011), Game location and team quality effects on performance profiles in professional soccer, Journal of Sports Science and Medicine, 10, 465-471.
- Lago, C., Casais, L., Dominguez, E. and Sampaio, J. (2010) ‘The effects of situational variables on distance covered at various speeds in elite soccer’, European Journal of Sport Science, 10: 103–9.
- Mackenzie, R., and Cushion, C. (2013), Performance analysis in football: A critical review and implications for future research, Journal of Sports Sciences, 31, 639-676.
- O'Donoghue, P. (2005), Normative profiles of sports performance, International Journal of Performance Analysis in Sport, 5, 104-119. - O’Donoghue, P. (2013). Sports performance profiling. In T. McGarry, P. O’Donoghue & J. Sampaio (Eds.), Routledge Handbook of sports performance analysis (pp. 127-139). London: Routledge.
- Sarmento, H., Marcelino, R., Anguera, M. T., Campanico, J., Matos, N., and Leitao, J. C. (2014), Match analysis in football: a systematic review, Journal of Sports Sciences, 32, 1831-1843.
- Shaw, J. and O’Donoghue, P. (2004) ‘The effect of score line on work rate in amateur soccer’, in P. O’Donoghue and M.D. Hughes (eds), Performance Analysis of Sport VI (pp. 84–91). Cardiff: CPA UWIC Press.
- Taylor, J. B., Mellalieu, S. D., James, N., and Barter, P. (2010), Situation variable effects and tactical performance in professional association football, International Journal of Performance Analysis in Sport, 10, 255-269.
- Taylor, J. B., Mellalieu, S. D., James, N., and Shearer, D. A. (2008), The influence of match location, quality of opposition, and match status on technical performance in professional association football, Journal of Sports Sciences, 26, 885-895.
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