Welcome to The CL Percentile Predictor (CLPP)

The CLPP is based on an Advanced Statistical Model which has been developed over the past six years after analyzing the correlation of mock scores against final CAT percentiles submitted by students. Go through the table below to know how close our predictions were!

Actual CAT 2015 %ile v/s CL's Predicted Percentile

USER_ID First Name Last Name Actual CAT %ile Predicted as per CL PP
1956458 Ayush Jain 99.63 99.79
1345843 Mudit Jain 99.99 99.94
1375624 Nitya Bansal 96.57 96.14
1246153 Little Jena 98.76 98.66
1800378 Shruti Saraf 97.37 96.96
1214340 Akshay Sharma 99.69 99.98
2007078 Shameek Datta 98.73 98.28
1699442 Piyush Mittal 98.96 99.39
1364791 Devan Goyal 97.59 98
986681 Abhineet Goel 99.59 99.9
1835171 Abhijit Routray 99.81 99.94
2207820 Pranav Joshi 98.87 98.82
991488 Keshav Bagri 99.64 99.95
2201010 Yash Rachh 96.31 96.79
1367496 Abhishek Garg 99.96 99.55
1222038 Nikhil Chauhan 99.53 99.98
2007742 Sanchit Garg 99.63 99.94
943420 Prannoy Ray 99.29 99.69
1861483 Karan Saini 99.73 99.58
1937219 Vidur Sharma 99.99 99.96
1229523 Rushil Bhutani 98.07 98.04
1332472 Nipun Jain 99.31 99.08
1949760 Shivam Kotwal 89.11 89.2
2001380 Ratnakar Patra 99.84 99.78
1932278 Mayank Sharma 96.86 96.65
1112462 Vandita Joshi 96.64 96.94
1780472 Nitish Kandpal 99.9 99.99
1811305 Shubham Verma 97.98 97.62
1121136 Anand Sisodiya 97.3 97.52
1779205 Raghav Sharma 99.87 99.65
1948412 Ayush Chandra 99.77 99.79
1002101 Ankit Saxena 98.52 98.88
2187402 Muktesh Jain 99.55 99.64
1986053 Rachit 99.45 99.54
1050162 Ambesh Talwar 97.85 97.8
1432704 Ashish Goyal 96.9 96.85
1095454 Sourabh Bansal 99.21 98.93
1945079 Deepu Jha 99.84 99.99
1946909 Mahesh Kundrapu 96.93 97.25
2127558 Rajat Khetan 99.54 99.99
2007858 Maneel Reddy 99.78 99.65
1086373 Lebin Sebastian 99.91 99.86
1943516 Mohit Pahuja 99.98 99.93
1365294 Aniruddha Bagchi 99.78 99.84
1955493 Vipul Naib 99.83 99.87
1093675 Shobhit Singhal 94.73 94.71
1922207 Apaar Singhal 99.92 99.62
1950852 Geet Aggarwal 97.03 97.02
1344240 Vaibhav Bhatnagar 99.72 99.73
1339766 Arnav Jindal 99.1 99.1
1718123 Ishan Dube 99.83 99.89
1805400 Utkarsh Srivastava 98.07 98.41
20099240 Atul Telang 98.77 99.26
1219856 Madhur Bhatia 91.4 91.06
1327168 Aakash Sinha 99.89 99.99
2175655 Bhoomika Goyal 99.12 99.62
1048938 Debesh Chand 99.39 99.3
1836165 Achintya Bhattacharyya 99.63 99.96
963692 Rahul Dubey 99.62 99.48
1996819 Anuj Verma 98.66 98.46
1811978 Abhishek Sharma 99.21 99.53
1284266 Amit Sharma 98.1 97.67
1928538 Achin Gupta 99.43 99.64
1953885 Raghu Chindukur 99.67 99.84
2004990 Anirudh Gupta 99.73 99.52
2006876 Lakshmi Priya 98.71 99.01
1805526 Shikhar Johar 98.57 99.04
1358347 Joydeep Sil 98.9 99.17
1791222 Ayush Goyal 97.18 97.31
1941003 Prerna Modi 99.56 99.16
1932000 Nakul Yadav 97.84 97.86
1983646 Anish Narula 96.58 96.38
1989164 Darpan Garg 99.24 98.8
2077113 Lavya Garg 99.97 99.99
2077206 Aditya Singh 99.72 99.97
1392495 Shivam Malhotra 96.3 96.17
1392736 Prashant Kumar 99.56 99.59
1239473 Navdeep Singh 98.92 98.94
1248019 Ronnie Philip 98.45 98.61
1386733 Vishesh Madaan 98.61 98.24
1407344 Satyajeet Gaur 99.06 98.62
1279259 Rahul Das 99.94 99.99
1330104 Rashmeka Banerjee 99.52 99.63
2187007 Archit Garg 99.75 99.61
1342621 Soumyadip Chakraborty 99.8 99.9
2038791 Suraj Srivastava 99.29 99.41
2026832 Ateev Gupta 99.93 99.95
1188965 Kshitij Arora 98.85 98.85
1954655 Soumya Patra 96.99 97.46
1028507 Sayan Poria 99.07 99.29
1929448 Aakash Ghosh 99.62 99.54
2134631 Aditya Priyadarshi 99.98 99.99
1361279 Aarushi Gupta 99.82 99.79
1947454 Pranav Aggarwal 99.83 99.9
1102963 Vivek Iyer 99.78 99.89
1711347 Ritwika Sengupta 96.01 96.15
1937532 Praneet Singh 99.89 99.9
1331084 Sankhadeep Saha 96.53 96.31
1987895 Gaurav Taneja 99.11 99.1
1220652 Yash Mahanot 99.72 99.95
1948643 Gursimran Ahuja 99.83 99.99
1940109 Akash Porte 59.48 59.77
1791829 Akshit Soni 99.42 99.5
1260773 Sayak Guha 98.98 99.21
1841725 Kaustabh Mallik 96.27 96.07
2077881 Kaustubh Kajgaonkar 97.08 97.55
1353179 Nikhar Mattu 99.92 99.92
1795758 Swapnil Jain 99.81 99.75
1357935 Rajan Kamboj 89.09 88.79
1983600 Ananth Radhakrishnan 99.85 99.79
2015058 Anish Karan Na 98.03 98.15
2060203 Anurag Wankhede 85.47 85.35
1990863 Agrim Aul 99.48 99.88
1952568 Ajan Roy 99.58 99.91
1204775 Maulik Chitransh 97.98 98.29
1709604 Akash 97.62 97.37
1379633 Amod Bansal 99.84 99.98
1223341 Anirban Mukherjee 99.53 99.87
1940448 Prasoon Priyank 99.51 99.92
1940368 Shaifali Mehta 94.98 95.34
1372179 Akshay Srivastava 99.52 99.99
1752600 Jinen Vora 95.51 95.03
2029724 Varun Bansal 98.21 98
1341658 Joydeep Das 99.2 98.89
1190529 Shubham Maheshwari 99.92 99.96
1928467 Shivvikram Singh 98.51 98.81
1382422 Prachi Maroo 74.31 74.79
2023246 Jishnu Sahney 99.61 99.16
1386344 Harsh Mehta 99.82 99.75
1124210 Alok Mishra 98.57 98.76
1207620 Sakshi Bhatia 97.73 97.65
1223507 Musunuru Praveena 98.84 99.31
1342573 Bira Agarwal 93.71 93.83
1385484 Sarang Modi 97.45 97.58
1058174 Aashay Gupta 99.16 99.47
1118600 Piyush Kedia 95.86 95.36
2135301 Shubham Kumar 99.53 99.09
1940914 Ashi Pachnanda 99.91 99.97
1238499 Niraj Mohata 99.83 99.84
1785538 Lakshay Pandhi 99.65 99.78
1784019 Ankit Singh 99.89 99.91
1261464 Ankit Choudhary 98.11 97.73
1229719 Dhiraj Bedi 99.85 99.51
2038585 Jeevan Pati 70.86 71.19
1869274 Ankur Ghosh 99.3 99.07
1167198 Abhishek Girotra 99.04 99.47
1944514 Shashank Banka 99.92 99.98
1377080 Varun Joshi 99.92 99.65
1354587 Pranav Mahajan 98.69 98.51
1955096 Akshata 99.86 99.75
2115257 Ayush Singh 98.46 98.45
1042528 Abhirup Roy 99.93 99.99
2021883 Devansh Neekhra 96.45 96.57
1188767 Harshad Marwaha 99.51 99.81
1427421 Surabhi Agrawal 96.51 96.57
1921495 Rahul Raj 99.93 99.99
1890008 Prateek Bhatia 90.19 90.28
2020736 Aditya J.V.R 99.15 99.64
1899170 Nirmalya Ghosh 98.26 98.72
2105099 Vedant Bagry 99.81 99.61
2077392 Anshul Bhatkar 89.41 89.2
1347879 Swagata Roychowdhury 99.6 99.9
1236744 Shreyansh Jain 99.59 99.87
2038557 Ayush Chhaparia 99.74 99.57
2048587 Snehal Singhania 99.73 99.92
2071918 Abhiroop Das 99.24 99.32
1809803 Bala Pushparaji 99.34 99.16
2119217 Priyanshi Garodia 99.92 99.97
1060103 Mehul Priyadarshi 99.72 99.98
1951803 Aashish Gupta 99.87 99.96
1367592 Aman Agarwal 99.52 99.54
1808417 Luv Saxena 99.84 99.98
1879322 Sachin Mishra 98.33 98.37
1925945 Arnab Maity 99.52 99.73
1346774 Udit Kukreja 98.07 98.06
1061504 Mittapalli Krishna 99.94 99.9
1955390 Rishikesh Bagri 99.78 99.5
1932013 Sumit Ranjan 99.85 99.98
1943088 Nitish Kumar 95.65 95.5
1770013 Kumar Priyank 94.72 95.03
1126060 Arnab Banerjee 99.82 99.96
1806577 Nishchay Budhiraja 100 99.95
1800712 Vatsal Sharma 97.59 97.84
1345342 Trisha Hegde 96.43 96.72
1098512 Rohit Choubey 98.47 98.39
1918745 Aurobindo Mohanty 99.07 98.94
1341924 Sumit Gupta 99.67 99.71
1378981 Megha Sharma 93.76 94.01
20095840 Niraj Lodha 99.96 99.63
966997 Murali G 98.95 99.41
1062142 Shreyash Kedia 96.79 96.34
1947470 Samarth Sharma 98.69 99.17
1345493 Pratishtha Bharvada 87.08 87.01
1885604 Sajal Gupta 99.26 99.03
1231431 Abhijoy Mustaufi 99.39 99.25
2006963 Rohan Saxena 99.78 99.74
1209956 Suraj Shaw 97.7 98.17
1200339 Juhi Jain 93.01 92.74
1708420 Himanshu Rawat 87.28 87.76
1996184 Himanshu Aneja 99.24 99.51
2015531 Ambuj Mishra 99.89 99.98
1379260 Dhruv Juneja 99.74 99.45
970538 Nitish Talwar 97.04 97.05
1939091 Rahul Bharadwaja 99.65 99.51
2008878 Prerita Nigam 88.85 89.27
2066493 Ankur Jindal 99.25 99.38
2025752 Anngad Singh 99.84 99.97
2229620 Aman Singhal 99.14 99.45
2077855 Pavankumar S 94.93 95.24
1344245 Chirag Dang 99.42 99.32
1434509 Nihar Agrawal 99.98 99.69
1334845 Ayan Saha 99.14 99.24
1713650 Shivank Goyal 96.3 96.77
1781175 Inderjeet Narwal 99.62 99.31
20099626 Sanjay Garg 99.63 99.26
1841125 Jiten C 76.67 76.49
1801608 Krishna Kolluri 99.82 99.99
2193389 Pritish Pani 99.83 99.96

 

The above model works not just with CL mock scores but also with mocks of other test prep organisations . If you have taken any mock of CL/TIME/IMS, you can submit the scores and see your CAT 2016 Predicted Percentile on the next page. The model predicts two percentiles - A Best-Case Percentile based on your best score and a Most Likely Percentile value based on a combination of your scores.
This tool is free for CL Test Series and Classroom students. However, if you are a student of other institutes, you have to purchase the CL's CAT Percentile Predictor for non-CL Mocks which is available at a very special price.

  • If the percentile predictor receives scores of multiple institutes then we provide a prediction based on a combination of mocks of all the institutes, as per an algorithm designed by our research team.
  • You need to take at least 4 Mocks of any institute to view the predicted percentile.
  • Your CL Mock scores will be automatically picked up (Proctored Mocks only)
  • You need to submit each AIMCAT/SIMCAT score - please do not enter any scores which do not represent your normal performance (e.g an unusually high or low score)

 

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Frequently Asked Questions


1. How accurate are these predictions? 

The predictions are based on statistical modeling done of students’ attempts and their corresponding CAT scores. For example, after CAT 2015 we analyzed the records of 753 students who had submitted their feedback about the CLPP. Over 35% of them reported that the predicted CAT percentiles based on their Mock Scores was within 0.5%ile of their actual CAT percentiles. In 2014, our model was able to predict the CAT Scores of 2014 to within 0.5 percentile of the actual CAT 2014 score for over 30% of the students who had submitted their final scores to CL. In CAT 2013 and CAT 2012, we had a similar success rate.

2. How does CL make predictions based on Mocks of other institutes?

The CLPP is a statistical model which has been modeled on data of tens of thousands of students each year. Our database of actual CAT percentiles scores obtained by students and their scores in Mocks of CL/TIME etc has built over years. Our academic team analyses all mocks in terms of the relative degree of difficulty and estimates their statistical parameters and feeds the same in the model. Over time, we have reached a level of confidence that we can do it for other Institutes too.



3. What do students say about CLPP?
User ID CAT %ile CL Predicted %ile Students Feedback
1166203 96.2 95.06 Its very close to the actual percentile. Keep up the good work !!
1172716 94.25 95.31 The percentile predictor is highly dependent on student's self assessment about the accuracy, thus the individual percentile can be highly unpredictable. But on the basis of the number of questions generally attended by students throughout, the accuracy of the predictor is undoubtedly good.
1207650 94.25 93.83 It is an awesome tool. Really helpful. CL is the best!
1079118 99.7 99.9 Quite accurate!
1123493 69.66 65.15 mock based prediction is good
1113154 93.54 96.99 In section 1 it did'nt predict correctly bt, section 2 prediction was close. So, a thumbs up from my side to thre predictor, but it needs improvement.
1047056 87.77 91.94 I have no problem with the CL predictor.It predicted my section 2 percentiles accurately
1166203 96.2 95.06 Its very close to the actual percentile. Keep up the good work !!
1136218 96.2 96.33 CL predictor is doing great
1220085 82.87 77.07 cl percentile predictor is just awesome..it had estimated the exact score of my cat.
1103692 99.9 99.81 It was great...:)
1164343 99.63 99.97 Good close estimates.
1058150 99.49 99.11 It is very accurate
1223799 88.93 88.19 Great Estimate by Career Launcher and Team. Cheers!
1039456 88.17 88.51 Its quite accurate.
1133497 93.78 94.69 It was nearly accurate
1185635 98.74 98.55 Quant percentile predictor is off the mark.
 


 

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