How to maximise your potential ATAR

The 2024 school year is just around the corner. Whilst most Year 11 and 12 students have already chosen their subjects for this year, it is not too late to change your mind (particularly if you are a Year 11 student this year). In this article, we take a look at historical data to forecast what subjects we expect will scale best for 2024 and 2025 high school graduates.

How is my ATAR calculated?

ATAR is the primary criteria to assess and compare the results of high school students for entry into an undergraduate university degree. More precisely, an ATAR is rank of where a student sits in the Queensland cohort, expressed on a 2,000 point scale from 0.00 (lowest) to 99.95 (highest), in increments of 0.05. ATARs equal to or below 30.00 are simply expressed as ‘30.00 or less’.

Although ATAR is a common Australia-wide measure of academic performance, the precise method of calculation differs from state to state.

The inter-subject scaling process (the Scaling System) is the process used in Queensland to express results across different subjects on the same scale, so that students’ results across different subjects can be compared fairly. For example, one might expect that, ceteris paribus, it is more difficult to obtain an A in Mathematical Methods than it is to obtain an A in General Mathematics. The Scaling System aims to provide a fair means of comparing those grades, notwithstanding the differences in difficulty.

Method

First, the Queensland Curriculum & Assessments Authority (QCAA) awards students a ‘raw result’ for a given subject. A raw result is simply the student’s grade for the subject, expressed out of 100.

Example: Jane is awarded 60 (out of 100) for Mathematical Methods. This is because she obtained marks of 40%, 50%, 70% and 80% (averaging 60%) in the four equally weighted summative assessments for Mathematical Methods.

Second, students’ raw results in a given subject are used to rank them against all other students who took that subject.

Example: Jane’s result of 60 for Mathematical Methods places her in position number 5,000 for Mathematical Methods (where position 1 is the worst result in Queensland).

Third, a students’ rank in a subject is converted into a percentile rank.

Example: Jane is ranked 5,000 for Mathematical Methods out of 15,000 students who took Mathematical Methods. Jane’s percentile rank would be approximately 33.3%, meaning she is better than approximately 33.3% of students.

Fourth, the process of obtaining a percentile rank for each subject is performed for all of a given student’s subjects. The sum of a student’s percentile ranks across all applicable subjects is divided by the number of subjects taken, producing Jane’s ‘polyrank’.

Example: Jane is percentile ranked:

(a)  65.4 for English;

(b)  33.3 for Mathematical Methods;

(c)  45.3 for Physics;

(d)  75.3 for Legal Studies; and

(e)  82.9 for Economics.

We can obtain Jane’s polyrank by averaging the above percentile ranks, which equals 60.44.

Fifth, once every Queensland student’s polyrank is determined, all polyranks are ranked, starting from 1 (being the lowest polyrank).

Example: Jane’s polyrank of 60.44 is ranked 15,000 out of 35,000 students in Queensland. This places her in the 42.86% percentile of students, based on her poly rank.

Sixth, the algorithm takes the raw result from one of a student’s top five (5) QCE subjects. The algorithm finds other students in Queensland who scored the same raw result in that subject. Once all of those students are found, all of their polyranks are averaged.

Example: Jane and four (4) other students (John, Eva, James and Olivia) achieved a score of 60 in Mathematical Methods. Their respective polyranks are as follows:

(a)  Jane: 60.44;

(b)  John: 58.40;

(c)  Eva: 72.33;

(d)  James: 59.09; and

(e)  Olivia: 61.32.

The average polyrank of students who scored 60 in Mathematical Methods is 62.32. This result indicates that a person who scores 60 in Mathematical Methods is slightly above average student across all of their other subjects, on average.

Seventh, this process of averaging polyranks for a given grade is executed for all students in a given subject. The averaged polyrank becomes the student’s new scaled score for the given subject. Because the student’s scaled score has changed, the student’s polyrank also changes.

Example: Across her five subjects, Jane’s new scaled scores for each subject are as follows:

(a)  62.01 for English;

(b)  62.32 for Mathematical Methods;

(c)  75.32 for Physics;

(d)  59.54 for Legal Studies; and

(e)  61.27 for Economics.

The average of these scores is 64.09. This will become Jane’s new polyrank.  

Eighth, the process set out in step six is repeated.

Example: Jane and three (3) other students (Sam, Oscar and Claire) have a scaled score of 62.32 for Mathematical Methods. Their respective polyranks are as follows:

(a)  Jane: 62.32;

(b)  Sam: 61.26;

(c)  Oscar: 67.32; and

(d)  Claire: 63.96.

The average of these scores is 64.16. This is Jane’s new scaled score for Mathematical Methods.

The process continues to repeat until the change in students’ polyranks between iterations becomes so small that it falls within the algorithms predetermined tolerance levels.

Example: After changes fall within tolerance levels, Jane’s scaled scores may look like the following:

		Scaled scores
Subjects taken	Raw scores	Initial	First iteration	Final iteration
English	73	65.4	62.01	61.54
Maths Methods	60	33.3	62.32	63.40
Physics	54	45.3	75.32	76.34
Legal Studies	75	75.3	59.54	59.35
Economics	80	82.9	61.27	60.23
Polyrank		60.44	64.09	64.17

Student who do not meet eligibility criteria to obtain an ATAR are removed from the final polyrank dataset.

If a student has done six (6) or more subjects, the five subjects which produce the highest combined score across the subjects. This is called a Tertiary Entrance Aggregate (TEA) score and is a number between 0 and 500 (this is because five (5) subjects are counted toward a student’s TEA score, each having a possible score between 0 and 100).

Once every student is allocated a TEA score, the TEA is converted into a percentile rank. The percentile rank ranks students TEA scores amongst one-another.

            Example

Student	TEA	Rank	Percentile Rank
Abbey	489.42	35,000	100.000%
Paul	489.19	34,999	99.998%
Amy	488.97	34,998	99.996%
…	…	…
Jane	320.86	23,917	68.334%
…	…	…
Benjamin	47.04	1	0.002%

A student’s TEA percentile rank is effectively their position in the state and ultimately determines what ATAR the student will obtain. Other considerations, such as the Participation Factor (discussed below), are incorporated into calculations before an ATAR is produced; however, these factors are by and large out of students’ control.

For completeness, we shall discuss the participation factor below:

Incorporating Participation Factor

There are a number of steps before a TEA percentile rank is converted into an ATAR.

First, the percentile rank obtained from the TEA is combined with the participation model to adjust for the overall distribution of students in the population. The participation model is a crucial component of the ATAR calculation process, designed to ensure that the final ATAR reflects not only a student's academic achievements but also their position within the broader population of Year 12 students. This model aims to account for variations in participation rates among different states, providing a fair assessment of a student's relative performance.

Factors influence the participation model are as follows:

(a)  Overall Participation Rate (OPR): The OPR is a fundamental metric representing the ratio of ATAR-eligible candidates to the total weighted population. It is calculated by dividing the sum of ATAR-eligible students aged 16-20 (E) by the potential Year 12 population (Y);

(b)  The participation curve is a mathematical function that characterises the relationship between a student's percentile ranking within the ATAR-eligible population the OPR:

(i)    for an OPR less than 0.25, the curve increases linearly up to x to accommodate a lower probability of ATAR eligibility;

(ii)   for OPR greater than 0.75, the curve decreases linearly from x to 1 to reflect a higher probability of ATAR eligibility; and

(iii) in the mid-range (0.25 ≤ OPR ≤ 0.75), the curve takes the form of a cubic spline function, incorporating a single knot at x = a where a = 1.5 – (2 x OPR).

The participation model is integral for two main reasons:

(a)  Balancing Allocation: The model influences the distribution of places across different ATAR bands. It ensures that a proportionate number of high-ATAR places are reserved for eligible students with higher abilities while leaving lower-ATAR places mostly for non-eligible students or those with comparatively lower abilities; and

(b)  Adjusting Percentile Rank (APR): The adjusted percentile rank (APR) is derived by multiplying the student's percentile rank (PR) by the participation model factor. This ensures that a student's position within the ATAR-eligible population aligns with the overall distribution, contributing to a fair representation of their achievements.

The final step involves scaling the adjusted percentile rank to the ATAR scale (0.00 to 99.95). This is achieved by mapping the adjusted percentile rank onto the ATAR scale.

ATAR = (Adjusted Percentile Rank (APR) / Maximum Adjusted Percentile Rank) × 99.95

The maximum adjusted percentile rank corresponds to the student with the highest TEA score in the population.

TLDR

The long and the short of it is that students aspiring to achieve particularly high ATARs will generally need to take subjects that other high achieving students are taking across the state.

Historical data released by the Queensland Tertiary Admissions Centre provides how each subject was scaled in 2022 for a given percentile.

The table (below) shows what a given raw result would be converted (scaled) to, based on what percentile the student was in.  

Subject	Result	25%	50%	75%	90%	99%
Accounting	Raw	58	70	81	89	97
	Scaled	60.34	76.22	86.39	91.25	94.49
Aerospace Systems	Raw	57	69	82	88	96
	Scaled	50.87	69.13	83.78	88.37	92.71
Agricultural Science	Raw	64	72	79	84	90
	Scaled	39.98	55.72	68.7	76.56	84.04
Ancient History	Raw	57	69	80	89	98
	Scaled	48.99	67.36	80.62	88.07	92.91
Biology	Raw	69	78	86	92	97
	Scaled	58.27	76.21	87.01	92.09	94.86
Business	Raw	55	67	78	87	96
	Scaled	49.27	65.03	77.13	84.59	89.93
Chemistry	Raw	72	82	90	95	99
	Scaled	73.69	89.1	95.06	97.05	98.06
Chinese	Raw	79	89	94	97	100
	Scaled	76.87	85.53	88.74	90.36	91.76
Chinese Extension	Raw	88	93	95	97	99
	Scaled	84.8	88.16	89.31	90.36	91.32
Dance	Raw	70	82	90	95	100
	Scaled	44.37	61.51	71.75	77.24	81.93
Design	Raw	53	64	77	86	96
	Scaled	47.08	60.84	75.02	82.58	88.73
Digital Solutions	Raw	56	69	83	92	97
	Scaled	57.95	76.25	88.87	93.48	95.21
Drama	Raw	64	75	86	93	99
	Scaled	47.09	64.13	78.23	84.85	89.13
Earth and Environmental Science	Raw	69	75	82	88	93
	Scaled	50.78	65.95	80.15	88.34	92.76
Economics	Raw	65	75	86	92	97
	Scaled	71.12	84.83	93.24	95.75	97.14
Engineering	Raw	55	68	80	89	96
	Scaled	62.17	80.67	90.8	94.95	96.88
English	Raw	77	86	94	98	100
	Scaled	54.7	70.65	83.73	91.12	95.34
English and Literature Extension	Raw	55	64	75	84	95
	Scaled	85.47	91.95	95.37	96.51	96.98
English as an Additional Language	Raw	60	72	84	92	99
	Scaled	51.54	66.78	81.39	89.21	94.73
Film Television and New Media	Raw	54	65	77	85	95
	Scaled	42.11	57.13	70.94	78.52	83.88
Food and Nutrition	Raw	54	65	77	85	95
	Scaled	44.79	59.4	73.57	81.04	87.96
French	Raw	73	82	89	94	98
	Scaled	86.45	93.8	96.73	97.95	98.59
General Mathematics	Raw	56	65	74	82	92
	Scaled	49.42	60.97	71.41	79.12	86.45
Geography	Raw	53	64	75	85	94
	Scaled	50.9	68.13	81.5	89.48	93.89
German	Raw	72	81	89	95	99
	Scaled	82.25	92.33	96.57	98.16	98.79
Health	Raw	54	64	76	85	95
	Scaled	46.4	59.53	73.53	81.74	88.38
Italian	Raw	71	83	91	95	97
	Scaled	80.23	91.32	95.21	96.46	96.97
Japanese	Raw	65	77	89	95	97
	Scaled	72.94	84.95	92.2	94.48	95.63
Korean	Raw	82	89	93	97	99
	Scaled	84.76	88.68	90.5	92.05	92.74
Legal Studies	Raw	55	66	78	87	96
	Scaled	53.49	68.56	81.42	88.09	92.58
Literature	Raw	68	80	89	95	100
	Scaled	72.31	86.89	93.02	95.5	96.91
Marine Science	Raw	60	68	75	82	90
	Scaled	43.9	59.3	71.51	81.22	88.95
Mathematical Methods	Raw	63	73	84	91	98
	Scaled	79.42	89	94.81	97.06	98.09
Modern History	Raw	62	73	84	91	98
	Scaled	57.33	75.03	87.05	91.81	94.93
Music Extension (Composition)	Raw	80	91	97	100	100
	Scaled	69.81	82.56	87.49	89.48	89.48
Music Extension (Performance)	Raw	79	90	96	99	100
	Scaled	64.63	80.63	86.71	89.09	89.8
Philosophy and Reason	Raw	61	75	87	94	99
	Scaled	66.54	83.96	92.31	95.12	96.49
Physical Education	Raw	58	68	79	87	96
	Scaled	46.58	60.62	74.2	81.92	88.31
Physics	Raw	73	82	90	95	98
	Scaled	75.2	89.11	95.19	97.17	97.95
Psychology	Raw	69	77	85	90	95
	Scaled	56.46	70.92	82.11	87.2	91
Spanish	Raw	66	79	87	91	97
	Scaled	77.14	91.42	95.58	96.85	98.12
Specialist Mathematics	Raw	67	79	88	93	98
	Scaled	87.9	95.16	97.65	98.43	98.96
Study of Religion	Raw	63	73	84	91	98
	Scaled	66.22	79.56	89.23	93.05	95.58
Visual Art	Raw	56	68	81	91	100
	Scaled	47.24	62.45	76.49	84.5	89.66

So, for example, if a student was in the 50th percentile of accounting students (i.e. received an average mark based on state-wide results, namely 70%), then the student’s grade would actually be considered to be 76.22%, because the scaling indicates that accounting is slightly harder (or, more accurately speaking, taken by better performing students) than the average subject.

What subjects one should take to maximise their ATAR really depends on what ATAR they are shooting for. If a student expects that they would be in or around the top 99% of students, then the best subjects based on 2022 data for the student to take would likely be (in order):

1.    Specialist Mathematics;

2.    German;

3.    French;

4.    Spanish;

5.    Mathematical Methods;

6.    Chemistry;

7.    Physics;

8.    Economics;

9.    English Literature and Extension;

10. Italian;

11. Literature;

12. Engineering;

13. Philosophy and Reason;

14. Japanese;

15. Study of Religion.

If a student expects that they would be in or around the top 90% of students, then the best subjects would likely be (also in order):

1.    Specialist Mathematics;

2.    German;

3.    French;

4.    Physics;

5.    Mathematical Methods;

6.    Chemistry;

7.    Spanish;

8.    English Literature and Extension;

9.    Italian;

10. Economics;

11. Literature;

12. Engineering;

13. Philosophy and Reason;

14. Japanese;

15. Digital Solutions.

You can see that simply by changing your target/expected across the board achievements, the scaling of the subjects changes. For example, while physics was the 7th best scaling subject for students in the top 1%, it was the 4th best scaling subject for those in the top 10%.

You may also see that a lot of languages rank exceptionally high. Students wanting to maximise their ATAR should be wary of taking languages they do not already speak, as it is often the case (particularly with the more niche languages) that a significant portion of the student population taking those subjects are fluent as will almost definitely attain better marks than you, lowering your ATAR. Ergo, just because a language ranks highly does not mean you should automatically take it.

Perhaps one observation worth noting about the 2022 data is that, despite a significant number of high achievers taking the ‘suicide six’ subjects (English, Specialist Mathematics, Mathematical Methods, Physics, Chemistry and Biology) with a view to maximising their ATAR, biology in particular did not scale particularly high in 2022. If you are only taking biology for the purpose of maximising your ATAR (as opposed to because you take a genuine interest in it, or it is a prerequisite/suggestion for your desired tertiary course), then it may be worth considering a swap to a language, economics, advanced literature course, or engineering.

Scaling is not the ‘be all and end all’

It is important that students chose their high school subjects in an informed manner, with at least a general grasp on historical trends of how the various subjects scale. However, to enjoy the benefit of a high scaling subject, students must actually excel in the subject. Accordingly, students should choose their subjects by asking themselves the following questions, among others:

(a)  What am I good at? What have I historically achieved good marks in?

(b)  What am I passionate about?

(c)  What subjects do I need to take to satisfy prerequisite requirements for entry into university courses I am interested in?

(d)  What scales high and will maximise my ATAR?

Note

Please note that this article summarises the calculation methods for Queensland ATARs, but makes a number of approximations for the purpose of simplicity. We are not responsible for any reliance on the data or methods described herein. For a complete and accurate dissertation on the calculative method of ATARs, please download the QTACs paper ‘Calculating the ATAR in Queensland’ by clicking here.

You can also access the QTAC ATAR Report 2022 by clicking here.