Skip to main content
Log in

Classification and legality analysis of bowling action in the game of cricket

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

One of the hot topics in modern era of cricket is to decide whether the bowling action of a bowler is legal or not. Because of the complex bio-mechanical movement of the bowling arm, it is not possible for the on-field umpire to declare a bowling action as legal or illegal. Inertial sensors are currently being used for activity recognition in cricket for the coaching of bowlers and detecting the legality of their moves, since a well trained and legal bowling action is highly significant for the career of a cricket player. After extensive analysis and research, we present a system to detect the legality of the bowling action based on real time multidimensional physiological data obtained from the inertial sensors mounted on the bowlers arm. We propose a method to examine the movement of the bowling arm in the correct rotation order with a precise angle. The system evaluates the bowling action using various action profiles. The action profiles are used so as to simplify the complex bio-mechanical movement of the bowling arm along with minimizing the size of the data provided to the classifier. The events of interest are identified and tagged. Algorithms such as support vector machines, k-nearest neighbor, Naïve Bayes, random forest, and artificial neural network are trained over statistical features extracted from the tagged data. To accomplish the reliability of outcome measures, the technical error of measurement was adopted. The proposed method achieves very high accuracy in the correct classification of bowling action.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Aginsky KD, Noakes TD (2010) Why it is difficult to detect an illegally bowled cricket delivery with either the naked eye or usual two-dimensional video analysis. Br J Sports Med 44(6):420–425

    Article  Google Scholar 

  • Ahmed A, Asawal M, Khan MJ, Cheema HM (2015) A wearable wireless sensor for real time validation of bowling action in cricket. In: IEEE 12th international conference on wearable and implantable body sensor networks (BSN), 2015. IEEE, pp 1–5

  • Akay M (2011) Intelligent wearable monitor systems and methods. US Patent 7,981,058

  • Altun K, Barshan B, Tunçel O (2010) Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit 43(10):3605–3620

    Article  MATH  Google Scholar 

  • Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Ambient assisted living and home care. Springer, pp 216–223

  • Ayu MA, Ismail SA, Matin AFA, Mantoro T (2012) A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition. Procedia Eng 41:224–229

    Article  Google Scholar 

  • Bashir S, Qamar U, Khan FH (2015) Bagmoov: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas Phys Eng Sci Med 38:1–19

    Article  Google Scholar 

  • Bedogni L, Di Felice M, Bononi L (2012) By train or by car? detecting the user’s motion type through smartphone sensors data. In: IFIP wireless days (WD), 2012. IEEE, pp 1–6

  • Casale P, Pujol O, Radeva P (2011) Human activity recognition from accelerometer data using a wearable device. In: pattern recognition and image analysis. Springer, pp 289–296

  • Chin A, Lloyd D, Alderson J, Elliott B, Mills P (2010) A marker-based mean finite helical axis model to determine elbow rotation axes and kinematics in vivo. J Appl Biomech 26:305–315

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1):155–176

    Article  MathSciNet  MATH  Google Scholar 

  • Frew E, McGee T, Kim Z, Xiao X, Jackson S, Morimoto M, Rathinam S, Padial J, Sengupta R (2004) Vision-based road-following using a small autonomous aircraft. In: IEEE aerospace conference proceedings, 2004, vol 5. IEEE, pp 3006–3015

  • Garcia-Ceja E, Brena R (2013) Long-term activity recognition from accelerometer data. Procedia Technol 7:248–256

    Article  Google Scholar 

  • Ghasemzadeh H, Jafari R (2011) Coordination analysis of human movements with body sensor networks: a signal processing model to evaluate baseball swings. IEEE Sens J 11(3):603–610

    Article  Google Scholar 

  • Ghasemzadeh H, Loseu V, Jafari R (2010) Collaborative signal processing for action recognition in body sensor networks: a distributed classification algorithm using motion transcripts. In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks. ACM, pp 244–255

  • Hall MA, Smith LA (1998) Practical feature subset selection for machine learning. In: 21st Australasian computer science conference, ACSC, 1998. Springer, pp 181–191

  • Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2007) GNSS—global navigation satellite systems: GPS, GLONASS, Galileo, and more. Springer-Verlag, Vienna

    Google Scholar 

  • Johnson G, Waid J, Primm M, Aggerwal R (2012) Ship attitude accuracy trade study for aircraft approach and landing operations. In: IEEE/ION position location and navigation symposium (PLANS), 2012. IEEE, pp 783–790

  • Mannini A, Sabatini AM (2010) Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2):1154–1175

    Article  Google Scholar 

  • Mannini A, Sabatini AM (2011) On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, 2011. IEEE, pp 3302–3305

  • MCC (2013) Laws of cricket. http://aucklandcricket.co.nz/Uploads/Club_cricket_files/2013_14/Laws_of_Cricket_2000_Code_5th_Edition_2013_-_changes_shown_in_yellow.pdf

  • Middleton KJ, Alderson JA, Elliott BC, Mills PM (2015) The influence of elbow joint kinematics on wrist speed in cricket fast bowling. J Sports Sci 33(15):1622–1631

    Article  Google Scholar 

  • Miguel-Etayo D, Mesana M, Cardon G, De Bourdeaudhuij I, Góźdź M, Socha P, Lateva M, Iotova V, Koletzko B, Duvinage K et al (2014) Reliability of anthropometric measurements in european preschool children: the toybox-study. Obes Rev 15(S3):67–73

    Article  Google Scholar 

  • Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52(3):239–281

    Article  MATH  Google Scholar 

  • Niu X, Zhang Q, Li Y, Cheng Y, Shi C (2012) Using inertial sensors of iphone 4 for car navigation. In: IEEE/ION position location and navigation symposium (PLANS), 2012. IEEE, pp 555–561

  • Perini TA, Oliveira GLd, Ornellas JdS, Oliveira FPd (2005) Technical error of measurement in anthropometry. Rev Bras Med Esporte 11(1):81–85

    Article  Google Scholar 

  • Preece SJ, Goulermas JY, Kenney LP, Howard D (2009) A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 56(3):871–879

    Article  Google Scholar 

  • Qaisar S, Imtiaz S, Glazier P, Farooq F, Jamal A, Iqbal W, Lee S (2013) A method for cricket bowling action classification and analysis using a system of inertial sensors. In: Computational science and its applications—ICCSA 2013. Springer, pp 396–412

  • Rousseeuw PJ, Hubert M (2011) Robust statistics for outlier detection. Wiley Interdiscip Rev Data Min Knowl Discov 1(1):73–79

    Article  Google Scholar 

  • Spratford W, Portus M, Wixted A, Leadbetter R, James DA (2015) Peak outward acceleration and ball release in cricket. J Sports Sci 33(7):754–760

    Article  Google Scholar 

  • Titterton D, Weston JL (2004) Strapdown inertial navigation technology, vol 17. IET

  • Uiterwaal M, Glerum E, Busser H, Van Lummel R (1998) Ambulatory monitoring of physical activity in working situations, a validation study. J Med Eng Technol 22(4):168–172

    Article  Google Scholar 

  • Wang J, Chen R, Sun X, She MF, Wu Y (2011) Recognizing human daily activities from accelerometer signal. Procedia Eng 15:1780–1786

    Article  Google Scholar 

  • Weaver BL, Theiss SK, Gorrell EM, Hagen KL, Bartol JJ, Brigham SE, Hill AL, Jennifer RY, Free MB (2011) Apparatus and method for processing data collected via wireless network sensors. US Patent 7,990,262

  • Williams N, Zander S, Armitage G (2006) A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification. ACM SIGCOMM Comput Commun Rev 36(5):5–16

    Article  Google Scholar 

  • Wixted A, James D, Portus M (2011) Inertial sensor orientation for cricket bowling monitoring. In: IEEE sensors, 2011. IEEE, pp 1835–1838

  • Wixted A, Portus M, Spratford W, James D (2011) Detection of throwing in cricket using wearable sensors. Sports Technol 4(3–4):134–140

    Article  Google Scholar 

  • Wixted A, Spratford W, Davis M, Portus M, James D (2010) Wearable sensors for on field near real time detection of illegal bowling actions. In: 1 of 1-conference of science, medicine and coaching in cricket 2010, p 165

  • Wu AD, Johnson EN, Kaess M, Dellaert F, Chowdhary G (2013) Autonomous flight in gps-denied environments using monocular vision and inertial sensors. J Aerosp Inf Syst 10(4):172–186

    Google Scholar 

  • Yeadon MR, King MA (2015) The effect of marker placement around the elbow on calculated elbow extension during bowling in cricket. J Sports Sci 33(16):1658–1666

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Salman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salman, M., Qaisar, S. & Qamar, A.M. Classification and legality analysis of bowling action in the game of cricket. Data Min Knowl Disc 31, 1706–1734 (2017). https://doi.org/10.1007/s10618-017-0511-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-017-0511-4

Keywords

Navigation